Llama 2: A Comprehensive Guide
In the vast arena of large language models (LLMs), a new contender, Large Language Model Meta AI 2 (Llama 2), has made its presence loud and clear. This LLM, developed by Meta AI, shows great potential for research and commercial use.
But what exactly differentiates it from other alternative language models – the benchmarks, and how can you get started with it? In this article, we will discuss all that and more to give you a comprehensive understanding of Llama 2.abc
Introduction to Llama
Llama 2 is a language processing technology similar to GPT 3.5 and OpenAI’s PaLM 2.
It employs a neural network with billions of variables, using the same transformer architecture and development concepts as its counterparts.
Here’s more about Meta AI’s Llama 2.
Parameter sizes for Llama 2
Llama 2 has three main variants in different sizes – 7B, 13B, and 70B. These three variants have different times and speeds.
Regardless of the model you choose, they can generate coherent text responses to any commands the user gives. The Llama models are also renowned for generating some of the safest responses to the given prompts, making them ideal for corporate applications.
What makes Llama 2 special
Llama 2 is one of the very few open-source language models that can generate human-like responses, much like GPT-3. However, it stands out because it is fully open-source, which means all individuals have complete access to the model for any purpose.
In contrast, models such as GPT-3 and Google Bard require API access, which limits interactions with the language model and raises security concerns for developers. That’s why many developers tend to stick with Llama 2 for developing new applications.
Llama 2 compared to other open-source LLMs
According to Meta AI’s benchmarks, Llama 2 models can beat the Falcon and MPT models on various benchmarks, including reasoning, coding, proficiency, and knowledge tests. In these tests, the higher the score is, the better the model performs.
|Test||Llama 2 (7B)||Llama 2 (13B)||Llama 2 (70B)||Falcon (7B)||Falcon (40B)||MPT (7B)||MPT (30B)|
|AGIEval (General intelligence of language models)||29.3||39.1||54.2||2`1.2||37.0||23.5||33.8|
|Winogrande (Common sense reasoning)||69.2||72.8||80.2||66.3||76.9||68.3||71.0|
|GSM8K (Math word problems)||14.6||28.7||56.8||6.8||19.6||6.8||15.2|
Llama 2 compared to mainstream language models
Llama 2 is better than the default GPT and PaLM 2 models because the primary use case for such language models is to generate clear and factual text responses.
To test the models’ creative ability and accuracy, we asked all three models to produce jokes.
Going over the results, it is clear that Llama 2 is capable of holding its own when it comes to generating creative responses. It also clarifies its stance on the subject, which highlights how it is engineered only to generate responses that are deemed safe for public use.
You can learn more about PaLM 2 model here
Up next, we tested the reasoning and basic math ability of these language models by asking a simple riddle that tests comprehension, logic, and basic math.
In this reasoning test, both Llama 2 and Google-PaLM fail and land at the same conclusion, and GPT 3.5 remains the only language model with the correct answer.
Ideal use cases for Llama 2
Llama 2 is predominantly used by individual researchers and companies because of its modest hardware requirements. Plus, it can handle specific applications while running on local machines.
With enough fine-tuning, Llama 2 proves itself to be a capable generative AI model for commercial applications and research purposes listed below.
“The number one thing I see this (Llama 2) being used for, is chatbots that belong to companies.” ~Igor Pogany
To use chatbots on your website or app, you need a reliable language model to prevent inappropriate responses. Because Meta AI prioritized safety when creating their fine-tuned model, Llama 2 is an excellent choice for customer support applications.
It has a strict filter for inappropriate prompts and quickly rejects generating harmful responses. This use case is reaffirmed by other content creators who have tested Llama 2 as well.
Fine-tuned models have already shown how they can be used to speed up the process of content generation in multiple ways. (You can learn how to fine-tune LLMs here.)
With Llama 2, you can generate web content, creative social media posts, witty tweets, and more. It can also help you brainstorm new ideas for your content by suggesting keywords, topics, or formats that match your preferences.
This helps overcome mental blocks. As an example, we asked Llama 2 to generate some catchy titles for a YouTube video, and you can see the result below.
Summarization and interpretation of existing data is something that AI excels at, which is why even smaller models such as Llama 2 7B are highly capable of analyzing data from sources such as reviews, reports, and articles to get accurate interpretations of trends, behaviors, and patterns that are present.
When it comes to weeding out the errors in written text, pre-trained language models perform the best. Typical grammar correction tools can sometimes misinterpret user intent and flag correct sentences as grammatically incorrect, but language models are seemingly immune to this.
This is because they are capable of identifying the user’s intended meaning, allowing them to eliminate any false flags and provide the most accurate result.
The frequency and volume of content uploads make it borderline impossible for human moderators to effectively monitor content.
Benefits and challenges associated with using Llama 2
Llama 2 has clear advantages in terms of cost-efficiency and strong performance compared to open-source LLMs. However, it falls short in certain areas, particularly in its coding abilities, due to its smaller size. Let’s explore these aspects further.
1. Hardware requirements
Llama 2 excels with minimal hardware demands. It performs well on consumer-grade hardware, including the top-tier Llama 2 70B model, which can run with just a couple of RTX 3090s.
These lower requirements make it easier for researchers to get started and speed up innovation.
As demonstrated by the benchmarks from Meta AI, Llama 2 beats almost all the other alternative open-source language models at comprehension, reasoning, general intelligence, math, and more by a healthy margin, all thanks to RLHF.
This shows that if you have a basic understanding of RLHF, you should be able to fine-tune the model to meet most of your requirements.
3. Operating costs
Unlike the popular models such as GPT 3.5, which charge you a certain amount for tokens, Llama 2 is completely free.
Users have free reign over the language model to shape it to fit their needs. The only condition is that you will have to get permission from Meta AI if your project amasses over 700 million monthly users.
Safety is absolutely essential when creating a public-facing product. Ideally, you would want your language model to be capable of eliminating any potentially offensive interactions. Llama 2 does exactly that, as demonstrated in the violation percentage tests conducted by Meta AI.
Its capability for safe responses makes it suitable for corporate use, as it can refuse inappropriate requests. To see how sensitive Llama 2 is to turning down a request from a user, we asked the model to write us a joke about clowns, which it promptly declined.
1. Parameter size
Llama 2 has fewer parameters compared to other LLMs. For instance, the GPT-3 LLM has 175 billion parameters, over double the size of Llama 2 70B.
The smaller number of parameters in Llama 2 can affect its contextualization and generative abilities and make it more sensitive to changes in its training data.
2. Coding ability
Llama 2’s coding abilities are inferior to GPT-3. Even generating the code for a basic snake game is borderline impossible on Llama 2 due to the lack of code training data found in models like Code Llama.
3. Preventative model behavior
Users of the Llama 2 language model report that it often outright refuses to answer a query if it deems it even mildly inappropriate. In fact, on open chat platforms that use Llama 2, the language model will constantly make a statement to keep the conversation civil and polite.
This can be very limiting in dynamic applications with many prompts. The strictness might hinder obtaining clear and concise answers.
4. Limited multilingual capability
Llama 2 has limitations in handling non-English languages due to its primarily English training data.
Models like PaLM from Google and Claude Instant perform better with multilingual tasks.
To demonstrate this weakness, we asked Llama 2 70B to write us a social media post about the benefits of Vitamin D supplements in the German language. And despite being explicitly said that the post needs to be in German, it generated the response in English with a couple of German words at the start.
How to use Llama 2
To test Llama 2’s capabilities, you can visit Hugging Face’s website, where they provide a chatbot window to interact with Llama 2 models such as Llama 7B, 13B, and 70B.
Alternatively, you can try Poe from Quora after signing up for a free account, where you can choose from various fine-tuned chat models to compare their performance.
Meanwhile, you may learn how do Large Language Models (LLMs) work?
Because Llama 2 is still a fairly new language model, there are constant updates for it from both developers and Meta AI. One example of this is Code Llama, trained on a colossal 500 billion tokens of code and code-related data.
Seeing how different specially trained variants of Llama 2 are being worked on, it is not long before we see other Llama 2 variants like Med-PaLM 2 or Sec-PaLM 2 in the near future.
What do you think the future holds for Llama 2?