Statistics

Meta Llama 2: Statistics on Meta AI and Microsoft’s Open Source LLM

Explore Meta Llama 2's AI prowess! Uncover its features, training process, legal aspects, and strategic partnerships. A deep dive into the dynamic world of LLMs awaits.

Jonathan Gillham

Discover the intricacies of Llama 2, Meta's groundbreaking large language model (LLM), in this comprehensive article. We will shed light on its key features, capabilities, and applications within the realm of artificial intelligence. Delving into its release history, parameters, and accessibility, we will also draw comparisons with other LLMs. The article navigates through its training process, the datasets it operates on, and addresses potential legal implications associated with its use.

Additionally, we will delve into strategic partnerships with industry leaders, security enhancements in Llama 2, and advancements over its predecessor, Llama 1. Our analysis extends to a comparison with major competitors in the market, a discussion on the distinctions between open-source and closed-source LLMs, and an exploration of the unique qualities of Code Llama—a modified version of Llama 2.

By the conclusion of this piece, readers will possess a comprehensive understanding of Llama 2, its capabilities, and its position within the dynamic landscape of AI.

1. Key Spotlight on Llama 2

1). Llama 2 is a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. (Source)

2). Llama 2 is free for research and commercial use. (Source)

3). Llama 2 is available through Amazon Web Services (AWS), Hugging Face, and other providers too. (Source)

4). Llama 2 runs on Microsoft Azure, Amazon Web Services, and other cloud infrastructures through platforms like Hugging Face. (Source)

5). Llama 2 is an upgraded version of Llama-1, trained on a fresh blend of publicly available data. (Source)

6). Meta launched 3 versions of Llama-2 with 7, 13 and 70 Billion parameters respectively. (Source)

7). The Llama 2 model is trained on a mix of publicly available online data. (Source)

8). Microsoft is Meta’s “preferred partner” for distributing Llama 2. (Source)

9). Across a range of external benchmarks, including reasoning, coding, proficiency, and knowledge tests, Llama 2 outshines other open-source language models. (Source)

10). Llama 2 was trained on publicly available sources including: Common Crawl (an archive of billions of web pages), Wikipedia, public domain books from Project Gutenberg, and more. (Source)

11). In February 2023, Meta announced LLaMA 1, which stands for Large Language Model Meta Artificial Intelligence. (Source)

2. All You Need to Know About Llama 2

What’s Llama 2 and Why Does it Matter?

  • Llama 2 is Meta's open source large language model (LLM). (Source)
  • It is the Facebook parent company's response to OpenAI's GPT models and Google's AI models like PaLM 2. (Source)
  • Llama 2 is a collection of pretrained and fine-tuned large language models that offers a variety of foundational models. (Source)
Large language model meta al llama 2
  • Llama 2 is freely available for almost anyone to use for research and commercial purposes. (Source)
  • Llama 2 also has versions focused on chat applications. (Source)
  • Llama 2 is an upgraded version of Llama-1, trained on a fresh blend of publicly available data.
  • Meta launched versions of Llama-2 with 7B, 13B, and 70B parameters. (Source)
  • Llama 2 is poised to be a well-known LLM among the public and developers. (Source)

When was Llama 2 Released?

  • Llama 2 was released by Meta via a research paper published in July 2023. (Source)
  • Llama 2 is the successor to Llama 1, which Meta introduced in February 2023. (Source)

How many Parameters does Llama 2 have?

  • Meta released several models of Llama 2, with three different sizes: 7B with seven billion parameters, 13B with 13 billion parameters, and 70B with 70 billion parameters. (Source).
The number of parameters of each llama 2's foundational model
  • This technique enables Llama 2 to offer a diverse range of models with solid benchmark performance relative to their size. (Source)
  • The greater the value of the parameter, the more accurate the model. (Source)
  • A larger parameter size indicates that the model was trained with a large number of corpora, resulting in more accurate and reliable responses. (Source)
  • The largest model, Llama 2 with 70 billion parameters, performs best across all benchmarks. (Source)
  • The fact that Llama 2 70B performs well is no great surprise, given the size of the model. But Llama 2’s smaller models also rank well relative to their model size. (Source)
  • While all are optimized for speed, the smaller sizes will run significantly quicker on lower spec hardware, such as smartphones. Even if they aren't quite as effective at generating plausible or accurate text. (Source)
Llama 2 foundational models versions
  • The smaller parameter volume and development potential of Llama 2 will draw a considerable B2B executive audience eager to create AI-infused services, software and programmable devices. (Source)
  • But training models with a large number of parameters consume energy and computing resources. So smaller size models offer efficiency opportunities for optimizing the models with as few parameters as possible. (Source)
  • Llama 2 models have a capacity of 7 billion to 70 billion parameters, while ChatGPT contains 175 billion parameters. This positions Llama 2 as LLMs optimized for applications with a smaller physical footprint, like a smart device. (Source)
The number of parameters of each llama 2's foundational models compared to that of chat gpt 3.5

How can Llama 2 be Accessed?

  • Llama 2 doesn't yet have a flashy, easy-to-use demo application like ChatGPT or Google Bard. (Source)
  • For now, the best way to use it is through Hugging Face, the platform that's become the go-to hub for open-source AI models. (Source)
  • Through Hugging Face, users can try out the following versions of Llama 2: Llama 2 7B Chat, Llama 2 13B Chat, Llama 2 70B Chat. (Source)
  • Llama 2 is available in the Azure AI model catalog, enabling developers using Microsoft Azure to build with it and leverage their cloud-native tools for content filtering and safety features. (Source)
  • Meta is also making the Llama 2 model available on Amazon Web Services - AWS. Customers can use Amazon SageMaker Jumpstart to deploy and perform inference. (Source)
  • Llama 2 models are immediately available in the us-east 1 and the us-west 2 regions of AWS. (Source)
  • Qualcomm is expected to make the Llama 2 model available on Snapdragon-powered mobile devices and desktops in early 2024. (Source)
  • Llama 2 is optimized to run locally on Windows, providing developers with a streamlined workflow as they deliver generative AI experiences to customers across multiple platforms. (Source)
  • Llama 2 is available for download on platforms like Microsoft Azure, Amazon Web Services, and Hugging Face. (Source)
  • To download Llama 2, users must agree to an “acceptable use” policy that prohibits using the LLMs to incite or plan violence, terrorism, or generate disinformation. (Source)

Is Llama Chat the same as Llama 2?

  • Llama 2 comes in two options, Llama 2 and Llama 2-Chat. (Source)
  • Llama 2-Chat is a fine-tuned edition of Llama 2, specifically optimized for dialogue applications and two-way conversations. (Source)
  • Llama Chat uses reinforcement learning from human feedback to ensure safety and helpfulness. (Source)
  • Llama Chat, leverages publicly available instruction datasets and over 1 million human annotations. (Source)
  • Meta released versions of this model with 7B, 13B, and 70B parameters. (Source)
  • Llama 2-Chat was additionally fine-tuned on 27,540 prompt-response pairs, which performed better than larger but lower-quality third-party datasets. (Source)

3. Understanding the Training Process and Datasets in Llama 2

What Data is Llama 2 Trained on?

  • According to Meta, Llama 2 has been trained on a mix of data from publicly available online sources, which does not include data from Meta's products or services. (Source)
  • The training data of Meta Llama is mostly from large public websites and forums (Source) such as: 
  1. Webpages scraped by CommonCrawl
  2. Open-source repositories of source code from GitHub
  3. Wikipedia in 20 different languages
  4. Public domain books from Project Gutenberg
  5. The LaTeX source code for scientific papers uploaded to ArXiv
  6. Questions and answers from Stack Exchange websites
The Training Data Of Meta LLama
  • Meta adds that it has made an effort to remove data from certain sites known to contain a high volume of personal information about private individuals. (Source)
  • During training, the words are converted into a numerical representation called tokens. (Source)
  • Llama 2 models are trained on 2 trillion tokens, which translates to a massive training dataset. (Source)
Llama 2 models are trained on 2 trillion tokens, which translates to a massive training dataset
  • All flavors and variants of the model support an input token size of 4K, which roughly translates to 3,500 words. (Source)
The input token size supported by all flavors and variants of Llama 2 models
  • Each token is a word or semantic fragment that allows the model to assign meaning to text and plausibly predict follow-on text. (Source)
  • According to Meta, Llama 2 has been trained on over 40% more data compared to the previous Llama version and outperforms other language models on reasoning and knowledge tests. (Source)
percentage difference in training data between llama 2 and its predecessor, llama 1
  • For AI alignment, reinforcement learning with human feedback - RLHF, was used with a combination of 1,418,091 Meta examples and seven smaller datasets. (Source)
  • The average dialog depth was 3.9 in the Meta examples, 3.0 for Anthropic Helpful and Anthropic Harmless sets, and 1.0 for five other sets, including OpenAI Summarize, StackExchange, etc. (Source)

How Many GPUs does it take to Train Llama 2?

  • Training models with a large number of parameters consume energy and computing resources. So smaller size models offer efficiency opportunities for optimizing the models with as few parameters as possible. (Source)
Number of gpus does it take to train llama 2
  • 100% of the emissions from training Llama 2, are directly offset by Meta’s sustainability program, and because Meta is openly releasing these models, the pretraining costs do not need to be incurred by others. (Source)

Can Users Customize Llama 2 with their own Datasets?

  • Windows Developers can fine-tune the Llama 2 model to meet their specific needs right on their PCs using the Windows Subsystem for Linux and powerful GPUs. (Source)
  • Users can run Llama 2 on Microsoft Azure, Amazon Web Services, and other cloud infrastructures through platforms like Hugging Face, where they can train it on their own data to generate the kind of text they need. (Source)
  • For users that want to create an LLM to generate article summaries in their company's particular brand style or voice, they can train Llama 2 with dozens, hundreds, or even thousands of examples. (Source)
  • Both the foundation models and the fine-tuned chat models of Llama 2 are designed to be further trained to meet its users’ specific needs. (Source)
  • You can also further fine-tune one of the chat-optimized models of Llama 2, to respond to your customer support requests by providing it with your FAQs and other relevant information like chat logs. (Source)

Can Llama 2 be used to Train other LLM Models?

  • Meta forbids Llama 2’s usage to train other models. (Source)

How does Meta intend to Improve Llama 2?

  • Meta has also created new initiatives to harness the insight and creativity of individuals, researchers, and developers around the world to get feedback on how the models are performing and how they might be improved. (Source)

Why did Meta not release the Data Set used to train Llama 2 at first?

  • The AI industry has been open about the training data used for models, as training data is key to how these models perform.  (Source)
  • This powerful technology must be understood, and its outputs must be as explainable and traceable as possible, so that if something goes wrong researchers can go back and fix things. (Source)
  • The original Llama was supposed to be restricted to research and academic projects, but the model was leaked within a few days, causing technical and regulatory headaches for Meta. Llama 2 skips that issue by going open source. (Source)
  • Meta refused to release information about the data set used to train Llama 2 and the model, so as to avoid the plague all LLMs face: a propensity to produce falsehoods and offensive language. (Source)
  • Developers can refer to the model card for more information or to begin writing their own code. The model card notes among the limitations the model is intended for use in English only. (Source)

Is Llama 2 fluent in all Languages?

  • Llama 2 was primarily trained in English with a bit of additional data from 27 other languages. (Source)
  • Most Llama 2 data is in English, meaning it will perform best for English-language use cases. (Source)
  • This further means that Llama 2 may not be suitable for use in other languages. (Source)
  • According to Meta, Llama 2’s pre-training data is made up of nearly 90% English. (Source)
Percentage of languages used to train llama 2
  • Other languages, including German, French, Chinese, Spanish, Dutch, Italian, Japanese, Polish, Portuguese, and others, collectively make up less than 2% of Llama 2’s training data, while “unknown” makes up more than 8% of training data. This includes programming code data. (Source)
Percentage of other languages used to train llama 2, aside from english

Does Llama 2 Hallucinate?

  • Llama 2, like all LLMs, will occasionally generate incorrect or unusable answers. (Source)
  • Llama 2 is more likely to "hallucinate" or just make things up when given simple prompts. (Source)

Are There Legal Challenges Associated with AI Model Training Using Public Information?

  • Publishers, authors, and other creators have realized their work is being used to train all these AI models without their permission. This has been a widely discussed topic in tech news and continues to be addressed without any resolution yet. (Source)
  • A slew of lawsuits are already challenging tech companies' right to use public information for AI model training (Source)
  • Comedian Sarah Silverman's complaint is probably the most famous so far. She sued OpenAI, the creator of ChatGPT, alleging that the AI system used content from her books without her consent, infringing on her copyright Source).
  • Google, another AI leader, does not like to pay for online content as this would undermine its highly profitable business model. (Source)
  • Google’s top lawyer Halimah DeLaine Prado has said US law "supports using public information to create new beneficial uses." This argument might prevail in court. (Source)

4. Security Enhancements in Llama 2

Is it Safe to use Llama 2?

  • A safety evaluation of the Llama 2-Chat model as compared to other open-source and closed-source models was carried out by human raters, who assessed the model's responses for safety violations across approximately 2,000 adversarial prompts. (Source)
  • These prompts included both single and multi-turn prompts. It shows that Llama 2 is the safest among all other models. (Source)
  • It is important to caveat these safety results with the inherent bias of LLM evaluations due to limitations of the prompt set, subjectivity of the review guidelines, and subjectivity of individual raters. (Source)
Safety assessment Llama 2-Chat vs. OpenClosed-Source Models

Is Llama 2 Accessible by Everyone?

  • Llama 2 users are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license. (Source)
  • However, big tech-giants with more than 700 million active users monthly are required to request for special permission from Meta to use Llama, so it's off limits for the likes of Apple, Google, Amazon, etc. (Source)
Tech giants with 700M+ monthly users must seek Meta's permission to use Llama 2

How does Meta ensure responsible Use of Llama 2?

  • Training an AI model on the open internet can lead to potentially offensive and racist content. (Source)
  • Meta says the launch of Llama 2 is accompanied by a number of resources to ensure responsible use. These include red-teaming exercises, a transparency schematic, a responsible use guide, and an acceptable use policy. (Source)
  • The developers also employed other training strategies, including reinforcement learning with human feedback (RLHF) to optimize the model for safe and helpful responses. (Source)
  • With RLHF, human testers rank different responses from the AI model to steer it toward generating more appropriate outputs. (Source)
  • The chat versions were also fine-tuned with specific data to make them better at responding to dialogue in a natural way. (Source)
  • Meta has certain conditions around the licensing of Llama 2 which do not meet the technical definition of open-source as stated by its governing body, the Open Source Initiative or OSI. (Source)
  • Along with the release of Llama 2, Meta has included a responsible use guide for developers. (Source)
  • Meta has certain conditions around the licensing of Llama 2 which do not meet the technical definition of open-source as stated by its governing body, the Open Source Initiative or OSI. (Source)
  • Llama 2 foundational models were trained on a data set curated to remove websites that often disclose personal data of people. It also resamples sources considered trustworthy. (Source)
  • One of Meta’s key objectives with Llama 2 is to enhance safety and transparency. The model has undergone rigorous testing and fine-tuning to minimize biases and improve responsible AI practices. (Source)
  • By leveraging user feedback and incorporating supervised fine-tuning and reinforcement learning techniques, Meta aims to mitigate potential risks associated with generative AI models. (Source)

What Challenges is Llama 2 faced with?

  • A main challenge to Llama 2 may come from Apple if it decides to release an open-source LLM. (Source)
  • In reporting the rumor of Apple’s AI development, Bloomberg News noted that Apple executives had not decided how a potential AI framework would be released to the public. (Source)
  • Microsoft’s investment in Llama 2 is a certain pressure for an Apple response to AI. (Source)

5. Strategic Collaborations: Meta's Alliance with Industry Leaders for Llama 2

What's the Buzz About Llama 2? Is Microsoft Partnering with Meta?

  • People and businesses have benefited from the longstanding partnership between Microsoft and Meta. (Source)
  • Both Meta and Microsoft are united in their commitment to democratizing AI and making AI models widely accessible, and Meta is adopting an open stance with Llama 2. (Source)
  • Microsoft and Meta are expanding their long standing partnership, with Microsoft as the preferred partner for distributing Llama 2. (Source)
  • On 18th of July 2023, Meta and Microsoft jointly announced their support for the Llama 2 family of large language models on the Azure and Windows platforms. (Source)
  • The deal with Microsoft will bring Llama 2 to the Azure model library and allow developers to build applications with it without paying a licensing fee, though they will have to agree to Meta’s terms and conditions. (Source)
  • Together Microsoft and Meta introduced an open ecosystem for interchangeable AI frameworks, and have co-authored research papers to advance the state of the art in AI. (Source)
  • Microsoft and Meta partnered to expand the usage of PyTorch, the current leading AI framework developed by Meta and the AI community. They stand as founding members of the PyTorch Foundation. (Source)
  • Microsoft and Meta recently joined a cohort of supporters that endorse the Partnership on AI’s framework for collective action in the creation and sharing of synthetic media. (Source)
  • Microsoft and Meta partnership extends outside of AI and into the metaverse to deliver immersive experiences for the future of work and play. (Source)
  • Now, with this expanded partnership, Microsoft and Meta are supporting an open approach to provide increased access to foundational AI technologies to the benefits of businesses globally. (Source)

Besides Microsoft, which Companies is Meta in partnership with?

  • Meta’s engineers developed and shared frameworks that are now industry standards — like React, a leading framework for making web and mobile applications. (Source)
  • The social network giant has also built partnerships with AWS, Hugging Face, Databricks, and surprisingly, Microsoft’s Azure which backs ChatGPT-maker OpenAI. (Source)
  • Qualcomm, a leading producer of smartphone systems-on-a-chip, announced a partnership with Meta to have Llama 2 running locally on Qualcomm-powered smartphones “starting in 2024.” (Source)
  • Qualcomm now says it’s entering the LLM fray with Llama 2—unveiling plans to bring Llama 2 to smartphones. (Source)
  • Redmond, known for its extensive tech presence, has indeed taken a significant stride in supporting Llama 2, announcing its support for the AI system in both Azure and Windows (Source).
6 companies meta is in partnership with, for llama-2 models

6. Unveiling the Upgrades in Llama 2 Compared to Llama 1

What is Llama 1?

  • Large Language Model Meta AI is a family of autoregressive LLMs, released by Meta AI in February 2023. (Source)
  • Large Language Model Meta AI - Llama 1, is the first version of the state-of-the-art foundational LLM that was released by Meta in February in 2023. (Source)
  • Meta has witnessed a huge demand for Llama 1 from researchers, with more than 100,000 requests for access to the LLM. (Source)
Meta's swamped with 100,000+ requests to unleash the power of Llama 1

How Many Parameters does Llama 1 have?

  • Llama 1 is an impressive collection of foundational models, composed of models with parameter sizes of 7B, 13B, 33B, and 65B (Source).
The number of parameters of each llama 2's foundational models
  • The Llama 1 7B parameters model is trained on 1 trillion tokens, while the 33B parameters and 65B parameters models, on 1.4 trillion tokens each (Source).
The number of tokens in trillion, that llama 1 models are trained on
  • Llama with 65B parameters produces less biased prompts compared to other big LLMs like GPT3-175B and OPT-175B. Higher score indicates higher bias. (Source)
Llama (65B params) crafts less biased prompts than giants like GPT-3 (175B) and OPT-175B

How does Llama 1 perform compared to other Large Language Models?

  • Llama 1 stands out due to its extensive training on trillions of tokens. (Source)
  • According to Meta, the Llama 1 model with 13 billion parameters outperforms GPT-3, which has 175 billion parameters on most Natural Language Processing benchmarks. (Source)
  • The largest model of the collection, Llama-65B, holds its own amongst other leading models in the field of natural language processing like Chinchilla-70B and PaLM-540B. (Source)
Llama 1 perform compared to other large language models
  • Meta Llama 1 performs better than GPT-3 in the truthfulness test used in both LLMs performance measurement. The higher the score, the better. (Source)
  • However, as the results show, LLMs still need improvement in terms of truthfulness. (Source)
Llms still need improvement in terms of truthfulness

What makes Llama 2 better than Llama 1?

  • Llama 2 is the follow-up to Llama 1 — a collection of models that could generate text and code in response to prompts, comparable to other chatbot-like systems. (Source)
  • Meta claims that Llama 2’s performance improves significantly over the previous generation of Llama models. (Source)
  • Llama 2 was trained on 40% more data than Liama 1, which includes information from publicly available online data sources. (Source)
  • Llama 2 has double the context length, making it 4,096 context length, from Llama 1’s 2,048 context length. (Source)
The context length of llama-1 compared to that of llama-2
  • Llama 2 pretrained models are trained on 2 trillion tokens compared to 1.4 trillion tokens for Llama 1. (Source)
Token-training gap between Llama-1 and Llama-2
  • As a result, Llama 2 outperforms Llama 1 and many other open-source LLMs significantly. (Source)
  • Llama 2’s fine-tuned models have been trained on over 1 million human annotations. (Source)
  • Llama 2 outperforms other LLMs like Falcon and MPT when it comes to reasoning, coding, proficiency, and knowledge tests. (Source)
  • Meta says it received over 100,000 requests from researchers to use its first model, but the open-source Llama 2 will likely have a far bigger reach. (Source)
  • Llama 1 released 7, 13, 33 and 65 billion parameters while Llama 2 has 7, 13 and 70 billion parameters. (Source)
Comparison of Llama 1 and Llama 2 Models and their respective Number of Parameters

7. Assessing Llama 2 Against Other Market Contenders

What Similarities does Llama 2 have with other LLMs?

  • Llama 2 is a family of LLMs like GPT-3 and PaLM 2. (Source)
  • All these LLMs were developed and work in essentially the exact same way; they all use the same transformer architecture. (Source)
  • They all also use the same development ideas like pretraining and fine-tuning. (Source)

How does Llama 2 compare to various Open Source Language Models in the Market?

  • Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests. (Source)
Llama 2 vs open source language models on many external benchmarks

Llama 2 vs ChatGPT-4, Bard and PaLM 2

  • Most of the popular LLMs, including OpenAI's GPT-3 and GPT 4, Google's PaLM and PaLM 2, Anthropic's Claude, are all closed source. (Source)
  • The research papers released by OpenAI and Google on their bots had little to no technical information about the models. In contrast, the Llama 2 is transparent. (Source)
  • This transparency has helped Llama 2 find unanimous appreciation among the AI community. (Source)
  • Llama 2 is free for research and commercial purposes as opposed to Google and OpenAI’s products, which is a huge plus for smaller entities and researchers. (Source)
  • Meta says that in a range of benchmarks, Llama 2 models perform slightly worse than the highest-profile closed-source rivals, GPT-4 and PaLM 2, with Llama 2 coming significantly behind GPT-4 in computer programming. (Source)
  • Human evaluators find Llama 2 roughly as “helpful” as ChatGPT. (Source)
  • Llama 2 answered on par across a set of roughly 4,000 prompts designed to probe for “helpfulness” and “safety.” (Source)
  • Researchers compared Llama 2 performance on various benchmarks, like the multi-task language understanding and TriviaQA reading comprehension dataset, to other open source and closed source models, including the big names like GPT-3.5, GPT-4, PaLM, and PaLM 2. (Source)
  • The 70B versions of Llama outperformed the other open source LLMs and are generally as good as GPT-3.5 and PaLM on most benchmarks, but don't perform as well as GPT-4 or PaLM 2. (Source)
  • Llama 2 has three versions of the model with 7-billion, 13-billion and 70-billion parameters. Google’s chatbot, Bard with 137 billion parameters, PaLM 2 model with 340 billion parameters. While 175-billion parameters for GPT-3.5. (Source)
Llama 2 vs. Bard vs. PaLM 2 vs. ChatGPT 3.5 Parameter Showdown
  • Llama 2 was also trained on a total of 2 trillion tokens. Google’s PaLM 2, on the other hand, was reportedly trained on 3.6 trillion tokens. The training data for OpenAI’s GPT-3.5 is still under wraps. GPT-4 was trained on 13 trillion of tokens on the other hand (Source)
Token Battle: Llama 2 vs. PaLM 2 vs. GPT-4 Ai Models
  • Generally speaking, the more tokens, the better when it comes to generative AI. (Source)
  • In terms of performance, Llama 2’s 70-billion model is closer to GPT-3.5 on benchmarks of language capability (MMLU, and math solving) GSM8K, but trails behind on coding parameters. (Source)
  • The study also found that Llama 2 is also far behind OpenAI’s benchmark AI model GPT-4 and PaLM-2. (Source)
  • Meta claims Llama 2 is on par with OpenAI’s GPT 3.5 in academic benchmarks, such as MMLU which measures an LLM’s knowledge across 57 STEM subjects, and GSM8K which measures an LLM’s understanding of math. (Source)
  • Meta's Llama 1 and Llama 2 base models, MosaicML Pre-trained Transformer models, and Falcon models were evaluated on established academic benchmarks. The evaluation process was guided by an internal library developed by Meta, ensuring the reliability of the results. (Source)
  • The benchmarks were categorized into several categories, including Code, Commonsense Reasoning, World Knowledge, Reading Comprehension, Math, and Popular Aggregated Benchmarks. (Source)
  • In the evaluation, the Llama 2 models notably outperformed and showed substantial improvements in most categories, confirming the enhanced capabilities of the new model. (Source)
Comparison Between mpt, falcon, LLama 1, LLama 2 Ai Models
  • Most small models that outperform Llama 2 on the Open LLM leaderboard are themselves based on Meta’s prior model, Llama. That suggests Llama 2 will race up the charts as developers in the open-source community apply their talents to Llama 2. (Source)
  • Open-source AI models, such as Meta’s Llama 2, offer accessibility, customization, and transparency, enabling developers to modify the underlying code according to their specific needs. (Source)

Is Llama 2 better than ChatGPT?

  • OpenAI, the leading player in the LLM industry, now faces increased scrutiny following Meta’s groundbreaking announcement of Llama 2. (Source)
  • With its enhanced performance and 40% more data compared to its predecessor, Llama 2 emerges as a formidable contender challenging OpenAI’s market dominance. (Source)
  • Llama 2 simply isn't as good as ChatGPT for many tasks, especially if you're using GPT-4. (Source)
  • Even though Meta has confessed that Llama 2 "lags behind" GPT-4, it's still a free competitor to OpenAI. (Source)
  • The Llama-13B model outperformed ChatGPT 3.5, which has a significantly larger parameter size of 175 billion, across most benchmark datasets. This accomplishment highlights Llama’s efficiency in delivering top-tier performance with significantly fewer parameters. (Source)
  • Although Llama 2 may not yet match the performance of OpenAI’s latest model, GPT-4, it offers developers and businesses a customizable and transparent alternative. (Source)
  • This flexibility paves the way for faster product development and innovation, making Llama 2 an attractive choice for specific use cases. (Source)

8. Code Llama: What You Need to Know

What is Code Llama?

  • Code Llama is a state-of-the-art LLM capable of generating code, and natural language about code, from both code and natural language prompts. (Source)
  • Code Llama is a code-specialized version of Llama 2 that was created by further training Llama 2 on its code-specific datasets. (Source)
  • Code Llama is built on top of Llama 2. (Source)
  • Code Llama is free for research and commercial use. (Source)

How Many Parameters does Code Llama have?

  • Meta released Code Llama in three sizes with 7B, 13B, and 34B parameters respectively. (Source)
Number of Parameters Of Each Code LLama 2 models
  • The three models address different serving and latency requirements. The 7B model, for example, can be served on a single GPU. (Source)
  • The 34B model returns the best results and allows for better coding assistance. (Source)
  • But the smaller 7B and 13B models are faster and more suitable for tasks that require low latency, like real-time code completion. (Source)
  • Each of these models is trained with 500B tokens of code and code-related data. (Source)
  • The 7B and 13B base and instruct models have also been trained with fill-in-the-middle capability, allowing them to insert code into existing code, meaning they can support tasks like code completion right out of the box. (Source)
  • The Code Llama models provide stable generations with up to 100,000 tokens of context. All models are trained on sequences of 16,000 tokens and show improvements on inputs with up to 100,000 tokens. (Source)

What are the different Versions of Code Llama?

  • Additionally, Meta further fine-tuned two additional variations of Code Llama: Code Llama - Python and Code Llama - Instruct. (Source)
  • Code Llama - Python is a language-specialized variation of Code Llama, further fine-tuned on 100B tokens of Python code. (Source)
  • Code Llama - Instruct is an instruction fine-tuned and aligned variation of Code Llama. (Source)
  • Code Llama - Instruct is fed a “natural language instruction” input and the expected output. This makes it better at understanding what humans expect out of their prompts. (Source)

Is Code Llama better than other LLMs?

  • Meta’s benchmark testing showed that Code Llama performed better than open-source, code-specific LLMs and outperformed Llama 2. (Source)
  • Code Llama 34B, for example, scored 53.7% on HumanEval and 56.2% on MBPP, the highest compared with other state-of-the-art open solutions, and on par with ChatGPT. (Source)
Benchmark Testing Of Code LLama Compared to Other LLMS
  • Code Llama supports common programming languages being used today, including Python, C++, Java, PHP, Typescript, Javascript, C#, and Bash. (Source)

9. The Latest Release from Meta: Code Llama 70B

What's Code Llama 70B?

  • Code Llama 70B is the largest and best-performing model in the Code Llama family (Source)
  • Code Llama is a state-of-the-art LLM capable of generating code, and natural language about code, from both code and natural language prompts. (Source)
  • Code Llama is released under the same community license as Llama 2 (Source).

When was Code Llama 70B Released?

  • Code Llama 70B was released by Meta on January 29, 2024. (Source)

What are the Variants of Meta’s Code Llama Models?

  • Code Llama is built on top of Llama 2 and is available in the same three versions as previously released Code Llama models (Source).
       ->  CodeLlama - 70B
    : the foundational code model;
       ->  CodeLlama - 70B - Python 70B: specialized for Python;
       ->  Code Llama - 70B - Instruct 70B
    : fine-tuned for understanding natural language instructions.
  • Code Llama - Python is a language-specialized variation of Code Llama, further fine-tuned on 100B tokens of Python code. (Source)
  • Code Llama - Instruct is an instruction fine-tuned and aligned variation of Code Llama. Instruction tuning continues the training process, but with a different objective. (Source)
  • The model is fed a “natural language instruction” input and the expected output. This makes it better at understanding what humans expect out of their prompts. (Source)
  • Meta released four sizes of Code Llama with 7B, 13B, 34B, and 70B parameters respectively. (Source)
Number of Parameters of each Meta's Code Llama Models
  • Each of these models is trained with 500 Billion tokens of code and code-related data, apart from 70B, which is trained on 1 Trillion tokens. (Source)
Code LLama Models Training
  • The 7B and 13B base and instruct models have also been trained with fill-in-the-middle capability, allowing them to insert code into existing code. (Source)
  • This means they can support tasks like code completion right out of the box. (Source)
  • The 34B and 70B models return the best results and allow for better coding assistance. (Source)
  • While the smaller 7B and 13B models are faster and more suitable for tasks that require low latency, like real-time code completion. (Source)
  • The Code Llama models provide stable generations with up to 100,000 tokens of context. All models are trained on sequences of 16,000 tokens and show improvements on inputs with up to 100,000 tokens. (Source)

Is Code Llama 70B available for free?

  • Code Llama is free for research and commercial use, same as previously released Code Llama models. (Source)

How does Code Llama 70B work?

  • Code Llama is a code-specialized version of Llama 2 that was created by further training Llama 2 on its code-specific datasets, sampling more data from that same dataset for longer. (Source)
  • Code Llama features enhanced coding capabilities built on top of Llama 2. (Source)

What are the Performance Metrics for Code Llama-70B?

  • To test Code Llama’s performance against existing solutions, Meta used two popular coding benchmarks: HumanEval and Mostly Basic Python Programming. (Source)
  • HumanEval tests the model’s ability to complete code based on docstrings and MBPP tests the model’s ability to write code based on a description. (Source)
  • The benchmark testing showed that Code Llama performed better than open-source, code-specific LLMs and outperformed Llama 2. (Source). 
  • For example, in the HumanEval test, Code Llama 70B accuracy reached 53%. For GPT 3.5, this figure was 48.1%, and for GPT 4 – 67%. (Source)
Meta's Benchmark Testing Of Code LLama 70B Compared to Other LLMS

10. Open-Source versus Closed-Source LLMs

What does it mean for a Language Model to be Open Source?

  • LLMs are the backbone of AI tools like chatbots. They're trained on extensive datasets, empowering them to mimic human language and even computer code. (Source)
  • When an LLM becomes open source, it's essentially available for anyone to access, use, and modify for their own ends. (Source)
  • Open source approach promotes transparency and access. (Source)
  • Open-source also improves safety and security because when software is open, more people can scrutinize it to identify and fix potential issues. (Source)

Open Source vs. Closed Source LLMs: Which is Better?

  • Open source LLMs allow researchers to study their parameters and output and identify better ways to operate models. (Source)
  • Open-source drives innovation because it enables many more developers to build with new technology. (Source)
  • By being so open with Llama, Meta is making it significantly easier for other companies to develop AI-powered applications that they have more control over. (Source)
  • Zooming in on the different types of AI models, open-source and closed-source, reveals distinct characteristics. Open-source AI models, such as Meta’s Llama 2, offer accessibility, customization, and transparency, enabling developers to modify the underlying code according to their specific needs. (Source)
Open Source vs. Closed Source Ai Models
  • Several pre-trained LLMs such as Bloom, Llama 1, and Falcon have been publicly released. These models deliver performance on par with closed pre-trained counterparts like GPT-3 and Chinchilla. However, none of these models can effectively replace closed "product" LLMs like ChatGPT, Bard, and Claude. (Source)
  • Running a large language model offline on a smartphone is something closed AI models like OpenAI’s GPT 3.5 and Google’s PaLM2 can’t handle. (Source)
  • OpenAI and Google provide LLMs as an API. An Internet connection is required to access the API, and customers are charged based on use. (Source)

Llama 2 Long: An Extension of Llama 2

What is Llama 2 Long?

  • Llama 2 Long is an extension of Llama 2, an open-source AI model that Meta released in the summer of 2023. (Source)
  • Llama 2 Long is a step towards building more general and versatile AI models that can handle complex and diverse user needs. (Source)
  • Llama 2 Long can learn from a variety of data sources and perform multiple tasks such as coding, math, language understanding, common sense reasoning, and conversational skills. (Source)

How’s Llama 2 Long different from Llama 2?

  • Llama 2 Long has been trained on more data that contains longer texts and has been modified to handle longer sequences of information. (Source)
  • This allows it to outperform other models such as OpenAI's GPT-3.5 Turbo and Claude 2, which have limitations on how much context they can use to generate responses. (Source)
  • Meta researchers added another 400 billion tokens of data that contained longer texts than the original Llama 2 dataset of 2 trillion tokens. (Source)
Comparison of the number of tokens in trillion used to train Lama 2 and LLama 2 long
  • They also tweaked the architecture of Llama 2 slightly, by changing the way it encodes the position of each token in the sequence. (Source)
  • They used a technique called Rotary Positional Embedding (RoPE), which maps each token to a point on a 3D graph that shows its relation to other tokens, even when rotated. (Source)
  • This helps the model produce accurate and helpful responses with less information and memory than other methods. (Source)
  • They reduced the rotation angle of the RoPE encoding from Llama 2 to Llama 2 Long, which enabled them to include more tokens that are far apart or less frequent in the model's knowledge base. (Source)
  • They also used reinforcement learning from human feedback (RLHF), a method where the AI model is rewarded for correct answers and corrected by human evaluators, and synthetic data generated by Llama 2 chat itself, to improve its performance on various tasks. (Source)
  • They also acknowledge the potential ethical and social implications of such models and call for more research and dialogue on how to use them responsibly and beneficially. (Source)
  • Llama 2 Long can generate high-quality responses to user prompts that are up to 200,000 characters long, which is equivalent to about 40 pages of text. (Source)
Llama 2 Long crafts top-notch responses up to 200,000 characters

Conclusion

Meta's Llama 2, an advanced large language model, significantly advances the field of artificial intelligence. Its open-source methodology promotes transparency and collaboration, allowing global academics and developers to modify it to their own needs. Despite potential challenges including legal concerns and hallucination dangers, continual development and Meta's dedication to safe use ensure Llama 2's continued sustainability.

Llama 2's strategic collaborations with industry leaders such as Microsoft, its adaptability to user-specific datasets, and its competitive edge over counterparts like ChatGPT make it an invaluable asset for both businesses and developers. The addition of Code Llama, designed for code generation, further extends Llama 2's utility, particularly in the field of software development.

The capabilities of Llama 2 demonstrate the significance of open-source AI models. They not only encourage innovation, but also prioritize safety and security, positioning Llama 2 favorably against competitors in the market. To sum up, Llama 2 marks a significant milestone in the development of large language models, providing a robust, customizable, and accessible tool for a wide range of applications. Its continuous evolution holds the promise of further innovations in the field of AI.

Jonathan Gillham

Founder / CEO of Originality.AI I have been involved in the SEO and Content Marketing world for over a decade. My career started with a portfolio of content sites, recently I sold 2 content marketing agencies and I am the Co-Founder of MotionInvest.com, the leading place to buy and sell content websites. Through these experiences I understand what web publishers need when it comes to verifying content is original. I am not For or Against AI content, I think it has a place in everyones content strategy. However, I believe you as the publisher should be the one making the decision on when to use AI content. Our Originality checking tool has been built with serious web publishers in mind!

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