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Large Language Models (LLMs) 101

Top LLMs of 2024: Only the Worthy

May 15, 20249 min read

This blog introduces the six most influential large language models in 2024.

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Introduction

In a world where change is the only constant, large language models (LLMs) represent the highest level of evolution in natural language processing. These highly sophisticated artificial intelligence programs have changed our relationship with technology and what can be done with language, comprehension, and production.

As we enter 2024, many claims about game-changing models among LLMs exist. But worry not! We’re here to give you an entertaining, truthful, and nonsense-free rundown on what will happen this year. Without delay, let's introduce the top LLMs of 2024.

OpenAI’s GPT-4

OpenAI's Generative Pre-trained Transformer (GPT) models have ignited the first wave of excitement in AI development. Among these models, GPT-4 stands out as a significant advancement following the success of GPT 3.5. This GPT series iteration introduces many enhancements, including heightened reasoning capabilities, advanced image processing, and an expanded context window capable of handling over 25,000 words of text.

Beyond its technical prowess, GPT-4 significantly advances emotional intelligence, enabling it to engage in empathetic interactions with users. This attribute is invaluable in use cases like customer service interactions, outperforming traditional search engines or content generators. Moreover, GPT-4 can generate much more inclusive and unbiased content, addressing pertinent concerns regarding fairness and impartiality. It also incorporates robust security measures to safeguard against data misuse or mishandling, fostering user trust and maintaining confidentiality.

OpenAI also provides multimodal models like GPT-4o, which can reason across audio, vision, and text.

Gemini: The Dark Horse in NLP

Google's Gemini is a language model distinguished by its unique Mixture-of-Experts (MoE) architecture. It addresses key challenges in many language model applications, particularly concerning energy efficiency and the necessity for fine-tuning. It encompasses three versions—Gemini Ultra, Gemini Pro, and Gemini Nano—tailored to diverse scales and objectives, each offering varying levels of intricacy and adaptability to effectively meet specific requirements.

The MoE architecture of Gemini selectively activates related components based on input, fostering accelerated convergence and heightened performance without imposing a substantial computational overhead. Furthermore, Gemini introduces parameter sparsity by updating designated weights per training step, alleviating computational burdens, shortening training durations, and reducing energy consumption—a significant stride toward fostering eco-friendly and cost-effective training processes for large-scale AI models.

The latest iteration, Gemini 1.5, builds upon the foundation of its predecessors, presenting optimized functionalities such as an expanded context window spanning up to 10 million tokens and reduced training compute demands thanks to its MoE architecture. Among its achievements is its proficiency in managing long-context multimodal tasks and its ability to demonstrate improved accuracy in benchmark assessments like 1H-VideoQA and EgoSchema.

Cohere for Coherence: NLP’s New Favorite

Cohere is another innovative language model that brings fresh perspectives to understanding and generating human-like text. It offers a myriad of applications for solving real-world challenges, such as content generation and sentiment analysis.

One of Cohere's standout features is its ability to swiftly produce articles, blogs, or social media posts based on keywords, prompts, or structured data provided to it. This functionality proves especially beneficial for time-strapped marketers seeking engaging content promptly, as Cohere adeptly crafts titles, headlines, and descriptions, significantly streamlining manual efforts.

Moreover, Cohere excels in sentiment analysis, harnessing the power of natural language processing (NLP) to discern the emotional tone—positive, negative, or neutral—embedded within a given text. This capability empowers businesses to gauge customer sentiments regarding their products or services through reviews and feedback. Additionally, it enables organizations to grasp public sentiments on politics or sports, aiding in campaign planning by ensuring alignment with prevailing preferences.

Falcon: Speed Meets Accuracy

Developed by Training Infrastructure Intelligence (TII), Falcon has earned acclaim for its speed and accuracy across various applications. It offers two primary models: Falcon-40B and Falcon-7B, both of which have demonstrated impressive performance on the Open LLM Leaderboard.

The Falcon models feature a tailored transformer architecture, focusing solely on decoding while integrating innovative components such as Flash Attention, RoPE embeddings (Position Encodings learned with Random Permutation), Multi-Query Attention Heads, Parallel Attention layers, and Feed-Forward Layers. These enhancements significantly enhance inference speed, surpassing GPT-3 by up to five times during testing phases where single examples are processed sequentially.

Despite requiring 75% less computing power than GPT-3 during pre-training, Falcon 40 still demands approximately 90GB of GPU memory. However, the requirement was reduced to about 15 Gigabytes for fine-tuning or running inference on consumer-grade laptops. Notably, Falcon excels in tasks like classification or summarization, prioritizing speed without compromising quality, making it a top choice in scenarios where swift completion is paramount.

Mixtral: The Jack of All Trades

Mixtral is a language model developed by Mistral AI that has gained significant popularity due to its wide range of NLP applications. Its design and functionality make it a good fit for enterprises and developers who need an all-inclusive solution to language problems. Mixtral can handle language-based tasks concurrently, like writing essays, generating summaries, translating languages, or even coding, underscoring its applicability in various contexts. The most impressive thing about this model is its ability to adapt to different languages and situations, enhancing global communication and enabling service provision for diverse populations.

From a technical perspective, Mixtral operates on a Sparse Mixture-of-Experts (SMoE) architecture, optimizing efficiency by selectively activating related components within the model for each task. This targeted approach reduces computational costs while simultaneously boosting processing speed. For example, Mixtral 8x7B boasts a substantial context window size of 32k tokens. This feature enables it to manage lengthy conversations adeptly and tackle complex documents that demand a nuanced understanding of context, facilitating detailed content creation and advanced retrieval augmented generation with precision and effectiveness.

Despite having many parameters, Mixtral offers cost-effective inference similar to smaller models, making it a favorite for businesses that require advanced NLP capabilities without incurring high computational costs. The ability to support multiple languages, including French, German, Spanish, Italian, and English, makes Mixtral an invaluable asset for international companies seeking global communication channels and content generation abilities.

Llama: The People’s LLM

Llama, a series of open-source language models developed by Meta, has been recognized as "The People’s LLM" for its commitment to accessibility and user-friendliness. This unique focus makes Llama models the preferred choice for those prioritizing data security and seeking to develop customized LLMs independently of generic third-party options. Among its iterations, Llama2 and Llama3 stand out prominently.

Llama2 features a suite of pre-trained and fine-tuned LLMs, with training parameters ranging from 7B to 70B. Compared to its predecessor, Llama1, Llama2 has undergone training on 40% more tokens and boasts a significantly extended context window. Moreover, Llama2 offers intuitive interfaces and tools, minimizing entry barriers for non-experts and seamlessly integrating with the Hugging Face Model Hub for effortless access to pre-trained language models and datasets.

A significant advancement over Llama2, Llama3 is a major leap forward. Pretrained and fine-tuned on datasets with parameters ranging from 8B to 70B, Llama3 exhibits enhanced performance in contextual understanding, reasoning, code generation, and various complex multi-step tasks. Furthermore, it refines its post-training processes, leading to a notable reduction in false refusal rates, improved response alignment, and increased diversity in model answers. Llama3 will soon be available to AWS, GCP, Azure, and many other public clouds.

Side-by-Side Comparison


Feature/Model	Mistral Large	GPT-3.5 Turbo Instruct	GPT-4	Gemini	Llama 2	Cohere(Command)	Falcon
Creator	Mistral	OpenAI	OpenAI	Google	Meta	Cohere	Talesfromtheloop
Price per 1M Tokens	$12.00	$1.63	$37.50	$10.50	$1.00(for llama 70B But varies for other models)	$1.44	$1.44
Input Token Price	$8.00	$1.50	$30.00	$7.00	$0.90(for llama 70B But varies for other models)	$1.25	$1.25
Output Token Price	$24.00	$2.00	$60.00	$21.00	$1.00(for llama 70B But varies for other models)	$2.00	$2.00
Throughput (tokens/sec)	30.3	116.4	19.7	43.8	42.2(for llama 70B But varies for other models)	28.4	500
Latency (TTFT in seconds)	0.37	0.55	0.53	1.23	0.38(for llama 70B But varies for other models)	0.35	0.35
Context Window	33k tokens	4.1k tokens	8.2k tokens	1.0M tokens	4.1k tokens (for llama 70B But varies for other models)	4.1k tokens	4096 tokens
Parameter Size	6B	175B	350B	40B (Base) & 7B (Lite)	70B(variable)	Variable	Variable, Optimized for Tasks
Speed (Tokens per Second)	High	High, ~100 tokens/sec	Very High, ~200 tokens/sec	5x Faster than GPT-3, ~500 tokens/sec	High, ~100 tokens/sec	Up to 5x Faster than GPT-3, ~500 tokens/sec	Up to 5x Faster than GPT-3, ~500 tokens/sec
Accuracy	High, ~97% on benchmark tests	High, ~97% on benchmark tests	Very High, ~98% on benchmark tests	Higher than GPT-3, ~98% on benchmark tests	High, ~97% on benchmark tests	Comparable to GPT-3, ~97% on benchmark tests	Higher than GPT-3, ~98% on benchmark tests
Energy Efficiency	High	Moderate, ~0.5 Joules per token	Improved, ~0.3 Joules per token	Very High, ~0.1 Joules per token	High, ~0.2 Joules per token	Very High, ~0.1 Joules per token	Very High, ~0.1 Joules per token
Multilingual Support	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Integration with Existing Systems	Offers APIs and SDKs	Integrate GPT-3.5 into Flask-based chat support with Hugging Face Transformers	Offers compatibility with TensorFlow and PyTorch	Enables easy integration with AWS Lambda and Google Cloud Functions	Offers SDKs for web and mobile apps	Cohere offers APIs compatible with Python, JavaScript, and Java	Falcon's RESTful APIs enable seamless integration into existing systems
Real-World Applications	Used in conversational AI and content generation	Used in a wide range of applications, from content creation tools to customer service bots	Works with TensorFlow and PyTorch. Active in academia.	Used in gaming for dynamic dialogue and in marketing for personalized emails	In smart home devices for voice commands and in automotive for infotainment systems	Applied in healthcare for document translation and in finance for automated reporting	Utilized in logistics for real-time route optimization and in retail for predicting consumer behavior
Accessibility	Offers cloud APIs and on-prem deployment	Demands substantial computational resources	Provides cloud-based solutions for broader accessibility.	Designed for scalable cloud deployment, adaptable to various project sizes and budgets.	Emphasizes SDKs for easy cross-platform integration.	Offers cloud-accessible APIs for cost-effective experimentation.	Balances power and accessibility with flexible cloud deployment

Conclusion: Choosing Your Champion

The models we've highlighted today stand out as the crème de la crème of 2024. From OpenAI's GPT-4 and its versatility to Cohere's laser-sharp focus on coherence, each of these LLMs offers something unique and game-changing.

But the real question is, which one is right for you? As you navigate the LLM landscape, it's crucial to consider your specific needs and use cases. Do you require lightning-fast performance for time-sensitive applications? Cohere's coherence might be your best bet. Or are you looking for an efficient, resource-light model for your mobile app? Gemini could be the perfect fit.

Ultimately, the choice is yours. But one thing is sure: the possibilities are endless with these top-tier LLMs at your disposal. So, what are you waiting for? It's time to unleash the power of language processing and take your business or project to new heights.

Abdelrahman Elgendy
A passionate technical writer who enjoys demystifying AI and machine learning concepts, making them accessible to everyone.

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