Generative AI

LLaMA 2 vs. GPT 4: Which One’s the GOAT and Which One’s Just a Llama?

1. Introduction

Large Language Models (LLMs) have emerged as one of the most potent tools in deep learning, revolutionizing diverse domains from creative writing to programming.

Their core principle is simple: train using vast datasets, often via auto-regressive transformers, and then fine-tune to adapt to specific tasks and domains. They transformed machine responses from robotic text into organic, almost human-like conversations.

GPT-4 can generate human-like text based on prompts, making it an invaluable asset in numerous applications, from chatbots to content creation. The sheer size and prowess of GPT-4 make it stand tall, marking a significant leap from its predecessors.

On the other side, we have LLaMA 2. Developed by Meta, LLaMA 2 is a testament to the power of open-source development in the AI community. It’s more than just a tool; it’s a communal effort. With its Human Feedback Enhancement Learning, LLaMA 2 offers a unique twist in the world of language models. What sets it apart is not just its impressive capabilities, but also its accessibility. By being open-source, LLaMA 2 invites the global community of developers to partake in its journey, refining it further for diverse applications, be it academic research, content creation, machine translation, or even sentiment analysis..

This leads to the burning question on every tech enthusiast’s mind: If LLaMA 2 is available for all to use and tweak, could it possibly replace the closed-source powerhouse that is GPT-4?


2. High-Level Overview of Each Model

Meta’s LLaMA 2 (available here) with up to 70B parameters outperforms other open-source models. When benchmarked against models such as EleutherAI’s GPT-Neo and GPT-J, LLaMA 2 demonstrated superior results in several Natural Language Processing (NLP) tasks. For instance, in the widely recognized SuperGLUE benchmark, LLaMA 2 achieved a higher score, indicating its advanced comprehension and response generation capabilities. Furthermore, meticulous efforts have been made to enhance LLaMA 2’s safety using specific data annotations, iterative evaluations, and extensive red-teaming.


The LLaMA 2 Architecture

In contrast, OpenAI’s GPT-4, a multimodal marvel, can juggle both image and text data, an asset for diverse applications like dialogue systems and text summarization. Its impressive performance in human-designed exams and traditional NLP benchmarks speaks volumes of its capabilities. Still, like its predecessors, GPT-4 is bound by certain limitations, cautioning users to be judicious in applications where high reliability is paramount.


3. Technical Dive


Model Size & Parameters

LLaMA 2 is available in configurations of 7B, 13B, and 70B parameters. In comparison, GPT-4 is expected to have 1.7 trillion parameters. This not only suggests that GPT-4 might have a more comprehensive understanding of the data it has been trained on, but it also implies a nuanced ability to produce outputs of increased diversity and complexity.


Training Data & Techniques

OpenAI’s GPT-3 was trained on 300 billion tokens, the number of training tokens for GPT-4 it has not been revealed by Open AI. However, estimations are that it was  trained on 13 Trillion tokens while Llama 2 was trained with 2 trillion “tokens” from publicly available sources. Such vast training data implies a more extensive knowledge base and an enhanced ability to recognize patterns. LLaMA 2’s training data drew from a diverse mix of publicly available sources, emphasizing the removal of potentially personal information. The model has also benefitted from data cleaning, data mix updates, and other technical enhancements which aids inference scalability.


Architecture Innovations

Both models operate on the transformer architecture foundation, a neural network framework that has become synonymous with NLP tasks. GPT-4 takes this a step further, allowing both text and image inputs, enhancing its ability to contextualize and generate appropriate outputs. LLaMA 2, building on its predecessor, introduced increased context length and grouped-query attention. Such architectural tweaks make the model more attuned to various inputs’ subtleties, optimizing its performance on diverse tasks.


Inference Speed & Efficiency

In terms of computational agility, LLaMA 2 edges out slightly, being faster and more resource-efficient. This efficiency is likely attributed to its architectural innovations, especially grouped-query attention, which has been specifically designed to enhance inference scalability. Grouped Query Attention provides a better tradeoff between accuracy and inference speed than its alternatives, Multi-Quey Attention and Multi-Head Attention. GPT-4, although robust with its parameters, might require more computational resources, making it potentially slower in comparison.


4. Usability & Ecosystem


Tooling and API

GPT-4 and LLaMa 2 offer stark contrasts in terms of their ecosystems and tooling. LLaMa 2, introduced as a cutting-edge open-access language model, is now seamlessly integrated into the popular Hugging Face platform. This means developers and researchers have direct access to both its base and fine-tuned models, enabling them to easily integrate LLaMa 2 into a myriad of applications.

LLaMa 2’s license seems open, letting most users do a lot with it. But, giant companies with over 700 million monthly users , like Google, need special permission from Meta to use it. Also, you can’t use LLaMa 2 to help other language models get better.

On the other hand, GPT-4 is a closed-source model, accessible through a commercial API to developers with a commendable track record.

If you’re a developer aiming to build and scale an application based on an LLM, opting for an open source


Community & Open Source

Community-driven development has always been the crux of rapid and diversified innovation. LLaMa 2, being open-source, garners immense potential benefits from this. Open-source models, combined with the versatility of the Hugging Face platform, ensure that developers and researchers worldwide can contribute to and leverage the advancements of the LLaMa 2 model.

In contrast, GPT-4’s ecosystem, while impressive, seems to lean more towards a centralized approach. The model, although advanced and potent, is only available to OpenAI’s paying users. The pricing model for GPT-4, based on “prompt” and “completion” tokens, further delineates its accessibility. OpenAI does make strides in collaborative advancements, as evidenced by Bing Chat, co-developed with Microsoft, which operates on GPT-4. However, the question remains on how OpenAI’s approach might impact broader community-based innovations in the future.

As a developer focused on creating and scaling an application powered by a large language model, choosing an open-source option such as LLaMA 2 over a closed-source alternative like GPT-4 offers notable benefits in terms of cost savings and flexibility in customization. For a comprehensive analysis of the advantages of open-source large language models, we encourage you to explore our detailed blog post on this topic.


5. Performance Metrics


Benchmark Scores

Recent benchmark data shows the performance capabilities of these models. In various academic benchmarks, such as MMLU and GSM8K, LLaMA 2 either met or surpassed scores from models like PaLM (540B). Moreover, according a recent experiment conducted by Anyscale, LLaMA 2 70B is approximately as factually accurate for summarization as GPT 4. However, there remains a clear performance gap between LLaMA 2 70B and the behemoth that is GPT-4, especially in specific tasks like the HumanEval (coding) benchmark.

These benchmarks are more than just numbers; they give us insights into how these models will function in real-world applications. These scores offer insights into how seamlessly these models can be integrated into industries, impacting their efficiency and effectiveness. For instance, higher scores in the TriviaQA or Natural Questions benchmarks hint at the model’s potential in building robust Q&A systems or virtual assistants.


Few-shot & Zero-shot Learning

One of the main challenges in AI is how models handle limited data scenarios. In these contexts, GPT-4 continues to outshine, particularly in the 5-shot MMLU benchmark. Such dominance positions it as the prime choice for intricate, mission-critical tasks or any project necessitating extensive creativity.

Speaking of creativity, GPT-4’s advanced intricacy and larger model size translate into an unparalleled ability to generate content that resonates deeply with human-like attributes. Whether tasked with poetry or prose, GPT-4 delivers with a flair that evokes the craftsmanship of a seasoned writer. In contrast, LLaMA 2, though proficient, offers outputs reminiscent of a more basic, school-level assessment.

A particularly intriguing feature of LLaMA 2 is its employment of Ghost Attention (GAtt). Developed by Meta, this technique augments the model’s capability to control dialogues across multiple conversation turns. Simply put, it streamlines how the model digests parts of a dialogue, ensuring more accurate responses. This innovation, combined with results from Meta’s human evaluation study on LLaMA 2, suggests that while LLaMA may trail GPT-4 in some areas, its unique methodologies offer a promising future.

One of the primary challenges in multi-turn dialogues with AI models is the loss of context. In simpler terms, as the conversation progresses, models often “forget” or overlook prior instructions, leading to inconsistencies in their responses. Imagine asking an AI to respond only using emojis, but a few turns later, it starts using text again — this is a manifestation of that problem.

Meta, recognizing this limitation, introduced Ghost Attention (GAtt) to tackle it. Here’s a deeper dive into how GAtt works:

Augmentation for Memory Retention: At its core, GAtt operates by artificially linking or “concatenating” the initial instruction (e.g., “respond using emoji only”) to all subsequent user messages in the conversation. This ensures that the instruction remains a “ghost” presence in the background, continuously influencing the AI’s responses.

Reinforcement Learning Integration: This concatenated dialogue, rich in context, is then sampled using Meta’s Reinforcement Learning with Human Feedback (RLHF) model. This step is crucial, as it helps the model internalize the importance of the instruction in the context of the overall conversation.

Fine-tuning through Rejection Sampling: Using the above context-rich dialogue samples, LLaMA 2 is fine-tuned. This process is similar to Rejection Sampling in machine learning, where samples that don’t align with the desired criteria are “rejected” or not considered. This ensures that the AI consistently generates responses that are in line with the initial instruction.

In essence, Ghost Attention acts as a gentle reminder to the model, ensuring that it doesn’t stray from the user’s directives, resulting in more consistent, relevant, and user-aligned responses in longer conversations.


6. Real-World Applications & Case Studies

GPT-4, with its enhanced language capabilities, has been adopted by diverse entities, including the likes of Morgan Stanley and Khan Academy. Its applications range from serving as a virtual assistant to aiding in preserving rare languages.

LLaMA 2 is new so adaptation may take some more time but it isn’t far behind in tasks like text generation, summarization, and extending text. Businesses have noted the potential of LLaMA 2, especially given its ability to produce high-quality outputs with fewer examples.


7. Limitations & Challenges

  • Generalization:  Every AI model’s core pursuit is to generalize knowledge – to extrapolate from known data to unknown scenarios effectively. While LLaMA 2 and GPT-4 display a commendable grasp of diverse subjects, their performance can sometimes be inconsistent across different tasks. Consider a scenario where a model trained predominantly in Western literature is asked about Eastern philosophies. The answers might lack depth or accuracy. It’s not just about the volume of data, but its diversity and quality.
  • Model Interpretability: As AI models grow in complexity, interpreting their decision-making process becomes challenging. While the research doesn’t dive deep into the interpretability of LLaMA 2 or GPT-4, it’s an area worth probing. Given the past concerns with models generating misleading or incorrect answers, understanding their reasoning pathway is crucial.


8. Which One’s the GOAT?

GPT-4 stands out with its sheer number of parameters, versatility, and ability to handle both text and image inputs. It’s built to be steerable and to interact with users in a way that mimics human comprehension closely. On the other hand, LLaMA 2, a product of Meta’s collaboration with Microsoft, is hailed for its multilingual functionalities. With the capability to understand and produce content in over 200 languages, LLaMA 2 is the go-to model for multilingual tasks.

Despite the trade-offs, the AI landscape is evolving at a rapid pace, and the introduction of these models represents a quantum leap in how we understand and deploy language models.

When reflecting on LLaMA 2 and GPT-4, it becomes clear that both models have their unique advantages. LLaMA 2 stands out for its efficiency, allowing for quicker responses with less computational strain. Its open-source nature is its golden ticket, granting researchers and developers alike the freedom to delve into its intricacies, experiment, and adapt it to myriad applications. This collaborative approach signifies a monumental shift in the AI realm, illustrating the vast potential of community-driven advancements.

Before the rise of LLaMA 2, there was skepticism about whether open-source language models could ever rival the capabilities of giants like GPT-4. And while LLaMA 2 might not have entirely bridged that gap, it certainly offers a promising glance into the future. It embrace a world where open-source models might not only match but possibly even outperform their closed-source alternatives like GPT-4 in creativity and functionality.


9. Final Thoughts About LLM Deployment Challenges

One of the main differences between OpenAI’s GPT-4 and Meta’s LLaMA 2 is that the latter model is open-source. As we’ve already mentioned above, a significant advantage of open-source models is that they can be deployed at scale more cost-effectively and with the option to optimize them to run faster even at scale. Once you work with an open-source model like Llama 2, you gain from being able to use tools for optimization and faster inference.

A prime example of such a tool is Deci’s Infery-LLM inference SDK. This SDK significantly improves LLM performance, enabling up to five times more throughput while maintaining accuracy. More importantly, it optimizes the use of computational resources. This means larger models can be run on more affordable GPUs, reducing overall operational costs. 

When integrated with LLaMA 2 or comparable LLMs like DeciLM, Infery-LLM delivers notable performance boosts. The chart below illustrates this by showing the throughput acceleration achieved on NVIDIA A10 GPUs when using DeciLM 6B with Infery-LLM. This is compared against the baseline performances of both DeciLM 6B and LLaMA 2, as well as LLaMA 2 used in conjunction with vLLM, an open-source library for LLM inference and serving. The data clearly shows how Infery-LLM effectively enables a switch from the more powerful but costly NVIDIA A100 GPUs to the budget-friendlier A10 GPUs without sacrificing throughput or quality, even on less resource-intensive hardware.

In conclusion, Infery-LLM is crucial in tackling latency, throughput, and cost challenges in LLM deployment, proving an invaluable tool for developers and organizations using these advanced AI models.

Experience the capabilities of Infery-LLM firsthand; click below for a live demo and explore its transformative potential.


10. References

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")