GenAI Series: A review of the Architectural Journey of LLMs: Key Milestones from 2017 to Present Day

Foundational Content -> Because, Knowing your History is Important

Apr 06, 2025

Large language models (LLMs) have revolutionized artificial intelligence, transforming how we interact with technology across countless domains. I am amazed at how much the ML world has changed from the time chatGPT launched. How we think about models, deployment, and maintainance has all changed. LLMs’ journey from academic research to mainstream applications represents one of the most significant technological evolutions of our time.

But it has honestly been hard to keep up.

In this blog post, I'll trace the fascinating development of LLMs, highlighting key architectural and model innovations, scaling breakthroughs, and performance advances that have shaped today's most powerful AI systems.

This post is going to be long and will go (almost) over all the most important models that I have seen coming up.

Below is the Posts’ TLDR image:

The Transformer Revolution (June 2017)

So, the story begins with the landmark 2017 paper "Attention is All You Need" by Google. This research introduced the Transformer architecture, which fundamentally changed natural language processing. If you have to read one paper from the many that come below, read and understand this one.

Before Transformers, sequence modeling relied primarily on recurrent neural networks (RNNs) like LSTMs and GRUs. While effective, these models processed text sequentially, making them computationally intensive and difficult to parallelize. They also struggled with long-range dependencies in text.

The Transformer architecture solved these problems with several key innovations:

Self-Attention Mechanism: This allowed the model to weigh the importance of different words in a sequence, regardless of their distance from each other.
Multi-Head Attention: By running multiple attention operations in parallel, the model could capture different types of relationships simultaneously.
Positional Encoding: This preserved word order information without requiring sequential processing.
Parallelization: Unlike RNNs, Transformers could process entire sequences simultaneously, dramatically speeding up training.

The original Transformer included both encoder and decoder components, making it ideal for sequence-to-sequence tasks like translation. This architectural foundation would soon be adapted and scaled to create increasingly powerful models. If you want to read more about transformers, take a look at this post, where I have tried to explain every part of transformer architecture in an easy-to-understand manner.

GPT-1: The Beginning of Generative Pre-Training (June 2018)

This is the first thing I remember from OpenAI now. It feels so long ago that this happened. In 2018, OpenAI released GPT-1 (Generative Pre-trained Transformer), bringing two significant innovations:

Decoder-Only Architecture: Unlike the original Transformer, GPT-1 used only the decoder portion, optimized for text generation.
Unsupervised Pre-Training + Supervised Fine-Tuning: GPT-1 was first trained on a large corpus of unlabeled text (the BooksCorpus dataset) to predict the next word in a sequence. This pre-trained model was then fine-tuned on specific supervised tasks.

This approach demonstrated that models could develop robust language understanding from unlabeled data and then adapt to specific tasks with minimal supervised training. Despite it just having just 117 million parameters, GPT-1 showed impressive results across multiple NLP benchmarks. Now an LLM model with 117 million parameters is chump change. How times change!!!!

BERT: Bidirectional context, masked language modeling (October 2018)

MLWhiz: Data Science, Machine Learning, Artificial Intelligence

One of the most quoted papers and an architecture that was a darling of the industry. Later, in 2018, Google introduced BERT (Bidirectional Encoder Representations from Transformers), taking a different approach with encoder-only architecture:

Encoder-Only Architecture: BERT used just the encoder portion of the Transformer.
Bidirectional Context: Unlike GPT's left-to-right processing, BERT could see context from both directions.
Masked Language Modeling: BERT was trained by randomly masking tokens and asking the model to predict them, forcing it to use context from both directions.
Next Sentence Prediction: BERT was also trained to predict whether two sentences naturally followed each other.

BERT excelled at understanding tasks rather than generation, establishing state-of-the-art performance on many language-understanding benchmarks. It was used in many classifiers across the industry and demonstrated that bidirectional context was crucial for deep language understanding.

GPT-2: Scaling Begins (February 2019)

And here Open AI started amazing us with their big guns(machines). OpenAI's GPT-2 marked the beginning of the scaling era. This was a time of bigger = better. With 1.5 billion parameters (10x larger than GPT-1), GPT-2 was trained on a more diverse 40GB dataset called WebText.

The key insight was that larger models with more diverse training data could achieve better results without architectural changes. GPT-2 demonstrated impressive zero-shot abilities, performing tasks without specific fine-tuning simply by providing instructions in its prompts.

This capability hinted at "emergent abilities" - skills the model wasn't explicitly trained for but developed as a result of scale. GPT-2 could generate coherent, contextually relevant text across various domains, showing that scale alone could drive significant improvements. It felt that the whole NLP field was going away from ML practitioners as models became bigger and bigger.

GPT-3: Massive Scale, Few-shot learning, Emergent Abilities (June 2020)

In 2020, in the midst of Covid, OpenAI's GPT-3 pushed scaling to new heights with 175 billion parameters - more than 100x larger than GPT-2. I was really amazed at this model’s capability being locked up in my house. This massive leap in scale produced surprising results:

Few-Shot Learning: GPT-3 could perform new tasks with just a few examples in the prompt.
In-Context Learning: The model could "learn" from examples provided in its context window without weight updates.
Broader Capabilities: GPT-3 showed abilities in code generation, translation, summarization, and even basic reasoning.

GPT-3 demonstrated that scaling could unlock emergent abilities not present in smaller models. It could perform tasks it wasn't explicitly trained for and adapt to new problems on the fly, suggesting that language models were developing a form of "general intelligence" within their domain. At this point, it felt that we had achieved AGI. Spoiler Alert: We didn’t and haven’t yet.

The Chinchilla Compute-Optimal Scaling Laws (March 2022)

It was high time that someone really did some research around about how much to scale? In 2022, DeepMind's research on the Chinchilla model challenged the prevailing wisdom on scaling. Previous work by Kaplan et al. suggested that with increased computational budgets, model parameters should grow faster than training data.

The Chinchilla paper showed this was suboptimal. For a fixed compute budget, models should be scaled roughly equally in parameters and training data. DeepMind demonstrated this by training Chinchilla (70B parameters) on 1.4 trillion tokens, achieving better performance than models several times its size.

This insight led to more efficient scaling strategies across the industry. Rather than just making models bigger, researchers focused on ensuring models were trained on sufficient a nd good data for their size. So, OpenAI needed to get away from scaling architectures for a while and get back to the training data for the good of the environment.

PaLM: Pathways to Efficiency (April 2022)

Not going to talk a lot about it, but the Google's Pathways Language Model (PaLM) with 540 billion parameters pushed the boundaries of scaling in 2022. Beyond its size, PaLM's significance came from its training infrastructure, the Pathways system, which efficiently distributed training across TPU pods. Again 540 B. That is honestly a lot.

ChatGPT: Conversational Revolution (November 2022)

And finally, we were in a changed world. In November 2022, OpenAI released ChatGPT, built on a version of GPT-3.5 that had been fine-tuned specifically for conversation. ChatGPT represented a significant milestone in making LLMs accessible and valuable to the general public.

The key innovations behind ChatGPT weren't just in the model architecture but in how it was trained:

Instruction Fine-Tuning: The model was trained to follow instructions in prompts, making it more helpful and easier to direct.
RLHF (Reinforcement Learning from Human Feedback): As detailed in the InstructGPT paper, human feedback was used to train a reward model, which was then used to further optimize the model via reinforcement learning.
Conversational Format: The model was specifically optimized for multi-turn dialogue, maintaining context across a conversation.

ChatGPT quickly became the fastest-growing consumer application in history, reaching 100 million users within two months of launch. It demonstrated that LLMs could be made accessible to non-technical users through a simple chat interface, bringing AI capabilities to the mainstream. This seems so long ago right now. I am unable to place it in my mind when I became addicted to using LLMs after this release. There was a time I used to think a lot, now I prompt a lot.

Claude 1.0: Focusing on Helpfulness and Harmlessness (December 2022)

I don’t remember Claude 1.0, to be honest, but right now, I use it the most for coding. Anthropic introduced Claude in 2022, with a public release in 2023. Claude was developed with a strong focus on being helpful, harmless, and honest - what Anthropic calls "Constitutional AI."

The key innovations behind Claude included:

Constitutional AI (CAI): Detailed in a research paper, this approach used AI feedback rather than solely human feedback to train models according to a set of principles or "constitution."
RLHF+: An enhanced version of reinforcement learning from human feedback, focusing on reducing harmful outputs while maintaining helpfulness.
Red-Teaming: Extensive adversarial testing to identify and mitigate potential misuse scenarios.

Claude models demonstrated that LLMs could be made more helpful and less harmful without sacrificing capability. But this model didn’t became very popular until future iterations.

Llama 1 Efficient Training, Open Research Model(February 2023)

And here is where Meta came into the mix. In February 2023, Meta released the first generation of LLaMA models, initially designed for research purposes only. This release included four models with sizes ranging from 7 Billion- 65 Billion parameters.

Unlike many commercial models at the time, LLaMA was trained on publicly available datasets, with Meta emphasizing careful curation and quality filtering. The training data included Common Crawl, C4, GitHub, Wikipedia, Books and research papers, and Mathematical and coding problems.

What made LLaMA particularly remarkable was its efficiency. The 13B parameter model outperformed GPT-3 (175B parameters) on most benchmarks, while the 65B model was competitive with models like Chinchilla (70B) and PaLM (540B).

Key innovations used in LLaMA 1 included:

Pre-normalization: Using RMSNorm before each transformer sub-layer
SwiGLU activation functions: Replacing ReLU for better performance. You can think of SwiGLU as an activation function where the input goes through two weight matrices, W and V. The first projection goes through the Swish Activation function, which is then multiplied with the other linear projection. Swish function is just a fancy name for a function as Swish(x) = x.Sigmoid(x).
Rotary Positional Embeddings (RoPE): More effective encoding of token positions. Rotary Position Embedding essentially encodes positional information directly into the attention mechanism through rotation matrices applied to query and key vectors

Let me know in the comments if you would like to understand SwiGLU and RoPE soon. I will try to explain them in detail.

While initially released with a non-commercial license and to approved researchers, the models quickly leaked online, leading to a thriving community of developers who fine-tuned and adapted LLaMA for various applications. This particularly mainstreamed LLMs, and everyone could now start an LLM with the provided weights on their own laptop. In my opinion, this was really huge, and Meta doesn’t get much credit for this.

GPT-4: Multimodal Capabilities (March 2023)

OpenAI kept on improving their models. In March 2023, OpenAI released GPT-4, a significant advancement over chatGPT. While the exact parameter count wasn't disclosed, GPT-4 represented several major improvements:

Multimodal Inputs: GPT-4 could accept both image and text inputs, allowing it to reason about visual content.
Improved Reasoning: GPT-4 showed substantially better performance on complex reasoning tasks, including coding, mathematical problem-solving, and standardized tests.
Extended Context Length: Later versions of GPT-4 could handle up to 128,000 tokens of context, allowing it to process entire books or codebases.
Enhanced RLHF: More sophisticated alignment techniques made GPT-4 more helpful, harmless, and honest than its predecessors.

GPT-4 demonstrated near-human or superhuman performance on various standardized tests and professional exams, showing that LLMs were becoming increasingly capable of complex reasoning tasks previously thought to require human intelligence.

PaLM 2: Improved efficiency and reasoning (May 2023)

In May 2023, Google released PaLM 2, which surprisingly outperformed its predecessor(PALM) despite having fewer parameters. This again demonstrated that architectural improvements and higher-quality training data could be more important than raw parameter count.

PaLM 2 excelled at reasoning tasks, mathematics, and code generation, proving that thoughtful scaling (not just bigger models) was the path forward.

Claude 2.0: Improved reasoning, 200K token context window(July 2023)

And this is where my favorite model Claude 2 landed officially on the scene with a new public-facing beta website, claude.ai. This particular iteration worked well by being on a similar level as GPT4 qualitatively.

Significant performance improvements were noted, including scoring 76.5% on the Bar exam's multiple choice section (up from 73% with Claude 1.3), above the 90th percentile on GRE reading and writing exams, and improved coding skills (71.2% up from 56.0% on the Codex HumanEval)
Input capacity was increased to 200K tokens, allowing users to process hundreds of pages of documentation or even books
Safety improvements made Claude 2 twice as effective at giving harmless responses compared to the previous version
And yes, I started using it extensively, and Claude and its various iterations have been my models of choice from then.

LLaMA 2 Extended Context Window, Commercial Use License(July 2023)

In July 2023, Meta addressed both the licensing limitations and performance gaps of the original models with LLaMA 2, a comprehensive update featuring Base Models(LLaMA 2 7B, 13B, and 70B) and Chat-Tuned Variants( LLaMA 2-Chat 7B, 13B, and 70B) optimized for conversational use cases

The most significant advancement in LLaMA 2 was its permissive license allowing commercial use. This dramatically expanded adoption, with companies able to legally incorporate the models into products and services.

Technical improvements included:

Extended context window: Doubled from 2048 to 4096 tokens
Additional training data: 40% more tokens (2 trillion total)
Grouped Query attention: For more efficient computation. As you might remember in transformer architecture, each attention head used to have 3 parameter matrices — K, V, and Q. Grouped Query Attention essentially splits attention heads into groups, with heads in each group sharing the same KV pairs but maintaining unique Qs. This reduces memory demands compared to Multi-Head Attention while preserving performance.
Improved tokenizer: Better handling of different languages and code

For the chat-tuned variants, Meta implemented a multi-stage fine-tuning process:

Supervised Fine-Tuning (SFT): Training on human-generated demonstrations
Reinforcement Learning from Human Feedback (RLHF): Optimizing toward human preferences
Safety fine-tuning: Additional training to reduce harmful outputs

LLaMA 2-Chat was particularly notable for being competitive with ChatGPT and Claude in conversational abilities while remaining open for commercial use. This led to a proliferation of applications and services built on the model.

Mixtral 8x7B: The Mixture of Experts (MoE) Approach (December 2023)

Other models came onto the scene at this point as well. I remember Falcon and Mistral here, but there were many others. Here, I would talk about the Mixtral 8x8B model, which adopted Mixture of Experts (MoE) architecture. Rather than having all parameters active for every token, MoE models:

Use a "gating network" to route input to specialized "expert" sub-networks
Activate only a subset of parameters for each token
Achieve better performance with greater parameter efficiency

Google's GLaM (Generalist Language Model) pioneered this approach in 2021, using 1.2 trillion parameters but activating only a fraction for any given input. This provided the benefits of a large model without proportional computational costs.

Models like Mixtral 8x7B have since popularized this approach in the open-source community, offering stronger performance than dense models of similar active parameter counts.

Gemini 1.0 Native multimodality (December 2023)

Google’s Bard was a huge Flop for me, but Google quickly pivoted to its Gemini family of models. Introduced in late 2023, these models marked a significant breakthrough as Google's first truly multimodal model family:

Native Multimodality: Built from the ground up to process text, images, audio, and video, rather than being adapted for multimodal inputs.
Mixture of Experts (MoE) Architecture: Used a sparse activation approach for greater efficiency.
Tiered Offering: Released in Ultra (highest capability), Pro (balanced), and Nano (on-device) variants.

While GPT 4 had come up with multimodal input capabilities, this model was the first time I was able to generate images and text with a single prompt.

Gemini 1.5 1M token context window (February 2024)

The 1.5 generation brought transformative improvements and introduced a 1M context for the first time:

Million-Token Context: Support for inputs up to 1 million tokens long, enabling reasoning over entire books, codebases, or hours of transcribed video.
Enhanced Recall Accuracy: Demonstrated 99.7% recall on documents up to 1 million tokens, maintaining 99.2% accuracy even with documents up to 10 million tokens.
Improved Instruction Following: Significant advances in complex multi-step instruction completion.
Flash Attention: In the later 1.5 releases, Gemini released 1.5 Flash, which is a lightweight variant of Gemini 1.5 optimized for speed and efficiency, using a mixture of experts (MoE) architecture with a reduced computational footprint. It offers a balance between performance and cost, making it ideal for latency-sensitive applications

Claude 3.0 Enhanced Reasoning, Improvedand multimodal capabilities (March 2024)

In March 2024, Anthropic released the Claude 3 family of models, representing a significant leap forward in LLM capabilities. The family included three variants: Claude 3 Haiku (fast and efficient), Claude 3 Sonnet (balanced performance), and Claude 3 Opus (highest capability).

Key innovations in the Claude 3 family included:

Enhanced Multimodal Capabilities: Claude 3 models could process and reason about images alongside text, analyzing charts, diagrams, documents, and photos with sophisticated understanding.
Improved Reasoning: The models demonstrated substantial advances in complex reasoning tasks, including math, coding, and analytical problem-solving.
Reduced Hallucinations: Claude 3 showed significantly lower fabricated information rates than previous models and competitors.
Balanced Alignment: The models maintained Anthropic's focus on helpfulness and harmlessness while achieving new performance benchmarks.

Claude 3 Opus, the flagship model, outperformed existing models on many academic and industry benchmarks, including MMLU (multiple-choice knowledge) and GSM8K (grade school math problems). The release established Anthropic as a leading competitor in the high-capability AI space.

LLaMA 3: Improved Reasoning, Coding, and Instruction Following(April 2024)

In April 2024, Meta released LLaMA 3, further enhancing capabilities across the board on Base and Chat-Tuned Models.

LLaMA 3 8B: More efficient than the previous entry-level model
LLaMA 3 70B: High-performance model challenging proprietary alternatives

LLaMA 3 represented a significant performance leap, with the 8B model achieving comparable results to LLaMA 2 70B on many benchmarks. Key improvements included:

Enhanced reasoning: Substantially better at complex problem-solving
Improved coding abilities: Performance comparable to specialized coding models
Better instruction following: More reliable responses to user directions
Multilingual improvements: Enhanced capabilities across languages

The performance gains were particularly significant for the smaller 8B model, enabling high-quality AI on more modest hardware and making deployment feasible in more resource-constrained environments. I remember how everyone was fine-tuning LLama models in a notebook at this time.

GPT-4o Optimized for speed, multimodal, reduced latency (May 2024)

In May 2024, OpenAI unveiled GPT-4o ("o" for "omni"), a revolutionary advancement in multimodal AI:

Real-Time Responsiveness: GPT-4o dramatically reduced latency, generating responses at human conversation speed - a significant improvement over previous models.

Seamless Multimodality: The model could process text, images, and audio simultaneously and respond in any combination of these modalities, enabling natural, fluid interactions.
Cost-Effective Performance: Despite its enhanced capabilities, GPT-4o was offered at the same price point as GPT-4, making advanced AI more accessible.
Vision Enhancements: GPT-4o demonstrated improved visual reasoning, the ability to analyze complex diagrams, interpret charts, and understand spatial relationships with greater accuracy.
Audio Understanding: The model could process spoken language directly, recognizing nuances in tone, accent, and emphasis that added contextual understanding.

GPT-4o represented a shift toward more natural human-AI interaction, moving beyond the limitations of primarily text-based interfaces. Its ability to process and generate multiple modalities simultaneously opened new possibilities for applications in education, accessibility, creative work, and professional services.

Claude 3.5 Sonnet Improved reasoning, tool use capabilities (July 2024)

In July 2024, Anthropic released Claude 3.5 Sonnet, an evolution of their earlier Claude 3 models:

Enhanced Tool Use: Claude 3.5 Sonnet demonstrated sophisticated abilities to use external tools, APIs, and information-retrieval systems, significantly expanding its problem-solving capabilities through MCP.

Artifacts: When a user asks Claude to generate content like code snippets, text documents, or website designs, these Artifacts appear in a dedicated window alongside their conversation.

I loved the artifacts when they came. This allowed the users to have better control over the chat without the chat just being a long piece of code. This segregation helped a lot when talking to the model.

LLaMA 3.2: Multilingual support and vision capabilities (September 2024)

The next addition to the family, LLaMA 3.2, represented Meta's ambitious expansion with:

Text-Only Models:

LLaMA 3.2 11B: Entry-level model with enhanced multilingual capabilities
LLaMA 3.2 90B: Mid-sized model with improved performance
LLaMA 3.2 405B: Ultra-large model competing with the most advanced proprietary systems

Multimodal Models:

LLaMA 3.2 Vision: Adding image understanding capabilities
LLaMA 3.2 Edge: Optimized for on-device deployment

LLaMA 3.2 introduced several major innovations:

Vision capabilities: Ability to process and reason about images alongside text
Expanded multilingual support: Comprehensive training across many more languages
128K token vocabulary: More efficient tokenization, improving compression and performance
On-device optimization: Models specifically designed for deployment on phones and other edge devices
Quantized versions: 4-bit and 8-bit variants for more efficient deployment

The expansion into vision capabilities was particularly significant, allowing the model to analyze and describe images in detail, answer questions about visual content, and perform visual reasoning tasks.

LLaMA 3.2 represents Meta's comprehensive effort to make sophisticated AI accessible across platforms, from powerful cloud servers to consumer mobile devices.

OpenAI o1: Specializing in Reasoning (October 2024)

In 2024, OpenAI released its o1 series of models, specifically designed to excel at complex reasoning tasks:

Internal deliberation: The models employ an extensive "chain-of-thought" process, engaging in thorough deliberation before generating a response
Exceptional performance: o1 achieves remarkable results on complex benchmarks, including reaching the 89th percentile on Codeforces programming competitions and scoring within the top 500 nationally on the AIME mathematics Olympiad qualifier
Scientific reasoning: Demonstrates Ph.D.-level accuracy on comprehensive physics, biology, and chemistry benchmarks
Variant options: Released in two versions - the flagship o1 model optimized for broad world knowledge, and o1-mini, a faster, more cost-effective alternative excelling in specialized domains like coding and mathematics

The o1 series represents a focused effort to develop models specifically optimized for high-stakes reasoning tasks where accuracy and reliability are paramount. This model keeps the chain of thought for the model hidden for competitive advantages, though.

Gemini 2.0 : Enhanced multimodal reasoning (November 2024)

The most recent generation Gemini 2.0 brought significant advancements:

Enhanced Multimodal Understanding: Improved performance across text, images, and video processing.
Superior Spatial Reasoning: Better object identification and contextual understanding in complex visual scenes.
Specialized Variants: Introduced the innovative Flash Thinking model, designed specifically for complex reasoning tasks.

The Gemini 2.0 Flash Thinking model represents a particularly notable innovation:

Transparent Reasoning: Unlike black-box models, it exposes its reasoning steps, providing visibility into its problem-solving approach.
Excellence in Complex Domains: Particularly strong in scientific and mathematical reasoning.
Code Execution: Ability to write and run code to solve problems, enhancing its utility for technical domains.

DeepSeek-R1: Pure reinforcement learning for reasoning (January 2025)

In 2024, DeepSeek introduced DeepSeek-R1, a model that demonstrated a novel approach to developing reasoning capabilities and sent NVIDIA stock crashing:

Pure Reinforcement Learning: Traditional AI models like early versions of ChatGPT and Claude needed tons of human-labeled examples showing "good reasoning." DeepSeek-R1 showed you could build strong reasoning skills without relying heavily on this expensive, human-labeled data. Instead of being explicitly taught with thousands of examples, the model learned through trial and error
Group Relative Policy Optimization (GRPO): This innovation eliminated the need for a critic model trained on labeled data, instead using a set of predefined rules to score model outputs across multiple rounds
Multi-Stage Training Process: To address initial shortcomings in output quality, DeepSeek developed a sophisticated process combining:
- Initial supervised fine-tuning on a small dataset
- Pure reinforcement learning to enhance reasoning
- Rejection sampling to create high-quality synthetic data
- Final fine-tuning combining synthetic and supervised data

This approach showed that AI systems could develop sophisticated reasoning skills with much less human supervision than previously thought necessary. It represented a shift toward models that could "teach themselves" more effectively.

DeepSeek-R1 achieved performance comparable to OpenAI's "o1" models on challenging mathematics competitions like the AIME, demonstrating that advanced reasoning capabilities could be developed through innovative training approaches rather than simply scaling model size and, hence, the NVIDIA stock crash.

Claude 3.7 Sonnet Advanced reasoning, extended thinking mode( February 2025)

In February 2025, Anthropic released Claude 3.7 Sonnet, introducing the industry's first "hybrid reasoning model." This groundbreaking advancement represented a significant evolution in how AI systems approach problem-solving:

Extended Thinking Mode: Claude 3.7 Sonnet pioneered a dual processing approach, capable of both near-instant responses and extended, step-by-step reasoning made visible to the user. This "reasoning mode" allowed the model to think through complex problems with unprecedented depth.
Enhanced Context Window: The model expanded its context processing capabilities to 200K tokens, enabling the analysis of massive documents and extensive conversation histories.
Transparent Problem-Solving: Unlike previous "black box" approaches, Claude 3.7 Sonnet exposed its thinking process, allowing users to follow its reasoning chain and better understand how it reached conclusions.
Improved Accuracy on Complex Tasks: The model showed significant improvements on challenging evaluations requiring multi-step reasoning, including mathematical problem-solving, code generation, and scientific analysis.
Balance of Speed and Depth: The hybrid approach allowed users to choose between quick responses for straightforward queries and thorough analysis for complex problems, optimizing both efficiency and reliability.

Released alongside Claude Code, Anthropic's dedicated coding tool, Claude 3.7 Sonnet represented a strategic response to reasoning-focused models like OpenAI's o1 series. The innovation highlighted an important trend in AI development: not just making models more knowledgeable but making them better thinkers with clearer visibility into their reasoning processes.

The model's ability to "show its work" addressed a key limitation of earlier LLMs, enhancing trust and enabling more effective collaboration between humans and AI on complex intellectual tasks.

LLama-4, MOE explored further, 10M token context window

While writing this post, Meta has now introduced a new suite of AI models called the "Llama 4 herd," which represents their latest advancement in multimodal AI technology. The announcement includes the key models:

Llama 4 Scout: A 17 billion active parameter model with 16 experts (109B total parameters), fitting on a single NVIDIA H100 GPU. It features an industry-leading 10M token context window and outperforms models like Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1.
Llama 4 Maverick: A 17 billion active parameter model with 128 experts (400B total parameters), beating GPT-4o and Gemini 2.0 Flash across many benchmarks while achieving comparable results to DeepSeek v3 with less than half the active parameters.
Llama 4 Behemoth: A still-in-training 288 billion active parameter model with 16 experts (nearly 2 trillion total parameters). It outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM benchmarks.

The main Innovations include:

First Meta models using mixture-of-experts (MoE) architecture
Native multimodality with early fusion of text and vision tokens
New "MetaP" training technique for setting critical model hyper-parameters
Enhanced pre-training on 200 languages, including over 100 with more than 1 billion tokens each
Improved vision encoder based on MetaCLIP
"iRoPE" architecture enabling the 10M token context window
Novel distillation techniques for knowledge transfer from Behemoth to smaller models

Key Trends in LLM Evolution

Looking back at this rapid evolution, I can see several key trends emerge:

Scale Matters: Increasing model size has consistently improved performance, though with diminishing returns.
Data Quality and Quantity: High-quality, diverse training data is as important as model size.
Architectural Efficiency: Innovations like MoE architectures have made models more efficient, and there is a need for many such innovations in the industry.
Emergent Abilities: Certain capabilities only appear beyond specific scale thresholds.
Multimodality: The integration of multiple data types is becoming standard with everyone doing it.
Reasoning Focus: Recent models show improved logical reasoning, not just pattern matching.
Context Length Growth: Maximum context windows have expanded from hundreds to millions of tokens.
Alignment Techniques: RLHF and similar approaches have become essential for creating helpful, harmless, and honest AI systems.
Transparent Reasoning: A shift toward models that expose their thinking process rather than just providing answers.

The Road Ahead

The evolution of LLMs continues at a rapid pace. Current research focuses on:

Further reasoning improvements: Models that can solve complex problems through deliberate, multi-step thinking, as seen in work like DeepSeek's research on reinforcement learning for reasoning
Tool use and agents: LLMs that can effectively use external tools and APIs
Efficiency innovations: Making models more accessible and less resource-intensive
Long-term memory: Moving beyond the limitations of context windows for true persistence

There is NO Conclusion this time →

The journey from the original Transformer to today's advanced models represents just seven years of research and development - a remarkably short time for such transformative progress. What began as a novel architecture for machine translation has evolved into systems with broad general intelligence within their domains.

As these models continue to advance, they're reshaping not just artificial intelligence research but how we interact with technology across virtually every industry. Understanding this evolution helps us appreciate both how far we've come and the exciting possibilities that lie ahead.

References:

And, here is the long list of references, which keeps on growing:

MLWhiz | AI Unwrapped

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