Large Language Models (LLM)
Large Language Models, or LLMs as they are known in the technical community, are artificial intelligence systems capable of understanding and generating text in a surprisingly human-like way. Imagine an incredibly intelligent assistant who has read practically everything ever written on the internet—books, articles, forums, technical documentation—and can converse on any topic with the naturalness of someone who truly masters the subject.
The Evolution of Architectures
Comprehensive LLM Timeline Update: 2024 - June 2025
February 2024
| Date | Event | Category |
|---|---|---|
| Feb 08, 2024 | Bard ➜ Gemini Transition – Google unifies Bard and Duet AI under the Gemini brand and launches an Android app + iOS integration. | Foundation Models |
| Feb 15, 2024 | Gemini 1.5 Pro – Up to 1 million token context window (2M in testing), MoE architecture, performance comparable to Gemini 1.0 Ultra. | Encoder-Decoder / MoE |
| Feb 15, 2024 | Sora Preview – OpenAI's text-to-video model (restricted access). Generates videos up to 1 min with complex scenes. | Multimodal Models |
March 2024
| Date | Event | Category |
|---|---|---|
| Mar 04, 2024 | Claude 3 Family – Haiku, Sonnet, Opus models (multimodal, 200K context). Opus surpasses GPT-4 on benchmarks. | Decoder-Only |
April 2024
| Date | Event | Category |
|---|---|---|
| Apr 18, 2024 | Llama 3 (8B & 70B) – 15T training tokens, >10M human-annotated examples. | Open-Source Models |
| Apr 2024 | Mixtral 8x22B – Open MoE architecture (176B total / 39B active). | Mixture-of-Experts |
May 2024
| Date | Event | Category |
|---|---|---|
| May 13, 2024 | GPT-4o "Omni" – Natively multimodal; 2× faster and 50% cheaper than GPT-4 Turbo. | Multimodal Models |
June 2024
| Date | Event | Category |
|---|---|---|
| Jun 13, 2024 | Official Adoption of the EU AI Act – First comprehensive regulation on AI. | Alignment / Regulation |
| Jun 20, 2024 | Claude 3.5 Sonnet – 2× faster than Opus; introduces "Artifacts" feature. | Decoder-Only |
July 2024
| Date | Event | Category |
|---|---|---|
| Jul 18, 2024 | GPT-4o Mini – Replaces GPT-3.5 Turbo; 128K context; 82% MMLU. | Decoder-Only |
| Jul 23, 2024 | Llama 3.1 – 8B / 70B / 405B versions; 128K context; Commercial license. | Open-Source Models |
September 2024
| Date | Event | Category |
|---|---|---|
| Sep 12, 2024 | o1-preview & o1-mini – First models with internal chain-of-thought. | Theoretical Advances |
| Sep 25, 2024 | Llama 3.2 Vision – First multimodal Llama; includes Llama Guard Vision for safety. | Multimodal Models |
October 2024
| Date | Event | Category |
|---|---|---|
| Oct 22, 2024 | Claude 3.5 Sonnet (New) – Model capable of operating graphical interfaces. | Hybrid Approaches |
December 2024
| Date | Event | Category |
|---|---|---|
| Dec 05, 2024 | o1 Full Release – 34% fewer errors than preview; limited access. | Theoretical Advances |
| Dec 09, 2024 | Sora Public – Sora Turbo; 1080p / 20s videos. | Multimodal Models |
| Dec 2024 | ChatGPT Pro – $200/month subscription with unlimited o1 access. | Foundation Models |
| Dec 2024 | Gemini 2.0 Flash – Focused on agents; native image/audio generation. | Multimodal Models |
| Dec 2024 | Llama 3.3 – 70B with 405B performance; support for 8 languages. | Open-Source Models |
| Dec 20, 2024 | o3 & o3-mini Preview – Successors to o1, still in testing. | Theoretical Advances |
2025
January 2025
| Date | Event | Category |
|---|---|---|
| Jan 21, 2025 | Stargate Project – $500B joint venture for AI infrastructure. | Foundation Models |
February 2025
| Date | Event | Category |
|---|---|---|
| Feb 24, 2025 | Claude 3.7 Sonnet – "Extended thinking" mode up to 128K tokens. | Hybrid Approaches |
| Feb 27, 2025 | GPT-4.5 "Orion" – Research preview; API deprecated in April. | Decoder-Only |
March 2025
| Date | Event | Category |
|---|---|---|
| Mar 25, 2025 | Gemini 2.5 Pro – #1 on LMArena; 1M token context. | Theoretical Advances |
April 2025
| Date | Event | Category |
|---|---|---|
| Apr 05, 2025 | Llama 4 Series – First Llama with MoE; Scout, Maverick, Behemoth versions. | Mixture-of-Experts |
| Apr 14, 2025 | GPT-4.1 Series – 1M context; Mini/Nano variants. | Decoder-Only |
| Apr 2025 | Gemini 2.5 Flash – Hybrid model with 0-24K token reasoning control. | Hybrid Approaches |
May 2025
| Date | Event | Category |
|---|---|---|
| May 22, 2025 | Claude 4 Family – Sonnet 4 & Opus 4 models; ASL-3 safety; parallel tool use. | Decoder-Only |
| May 2025 | Gemini 2.5 Pro Deep Think – Experimental mode for advanced math/code. | Theoretical Advances |
| May 21, 2025 | OpenAI acquires Jony Ive's firm (io) – $6.5B; focus on hardware + AI. | Foundation Models |
Trends & Observations
- Reasoning Revolution ⚙️ – Models with internal chain-of-thought (o-series, Claude 3.7, Gemini 2.5) make the thinking process a controllable hyperparameter.
- Omnipresent Multimodality – Text, audio, image, and video are becoming prerequisites for new releases.
- Open-Source Acceleration – Llama 3/4, Mixtral, and DeepSeek V3 show parity with (or superiority over) proprietary models.
- Concrete Regulation – The EU AI Act establishes deadlines (2025-2026) that influence global roadmaps.
- Infrastructure Scaling – Projects like Stargate highlight the magnitude of capital required.
- Safety by Design – Alignment techniques (Constitutional AI, Llama Guard Vision) are integrated into the training cycle.
The Democratization of Access
One of the most interesting stories in this field is that of Llama, developed by Meta [1]. Unlike other proprietary models, Llama was made available more openly, allowing researchers and developers around the world to experiment and create their own versions. It's as if, after years of only seeing luxury cars at the dealership, an affordable and high-quality option suddenly appeared.
Llama 2 [2] and its specialized versions, like Code Llama, showed that it was possible to create efficient models even with more limited resources. This opened the doors to a true explosion of innovation, with small companies and independent developers creating incredible solutions.
Gemma [3], from Google, followed this same philosophy of efficiency and accessibility. With only 2 or 7 billion parameters, it can rival much larger models on various tasks. It's a case of "size isn't everything"—sometimes, efficiency lies in the elegance of the solution, not in brute force.
The Quest for Natural Conversation
The Claude models [4], developed by Anthropic, brought a different approach to the table. Focused on more natural and human-aligned conversations, they represent a pursuit of AI that is not only intelligent but also ethical and trustworthy. It's like having a coworker who not only knows a lot but also has good sense.
The Architecture Behind the Magic
But how does all this magic happen? At the heart of virtually all these models is something called the Transformer architecture [5]. Without getting too technical, imagine a system capable of paying attention to different parts of a text simultaneously, weighing the importance of each word in relation to the others. It's as if, when reading a sentence, you could instantly understand not only the individual meaning of each word but also all the relationships and dependencies between them.
What's truly impressive is how these models can "emerge" abilities that were not explicitly programmed. As we increase the number of parameters—think of them as the model's "memory" and "experience"—completely unexpected capabilities arise. It's as if, by adding more neurons to an artificial brain, it suddenly begins to demonstrate creativity, logical reasoning, and even a sense of humor.
The Power of Parameters
To give you an idea of the exponential evolution of these systems: we started with models of millions of parameters, moved on to billions, and today we have models with hundreds of billions. Each parameter is like a small "adjustment knob" that the model uses to process information. The more parameters, the more nuances and subtleties the model can capture in human language.
More Than Words: Emergent Capabilities
What fascinates me most is how these models have begun to demonstrate skills no one expected. They can solve complex mathematical problems, write functional code, create creative analogies, and even demonstrate a primitive form of "reasoning." It's as if we are seeing the first signs of a genuinely different intelligence from our own, but complementary to it.
Let's pause for a moment to absorb this: we are talking about systems that learned to use language by observing patterns in text, without ever having experienced the physical world as we do. And yet, they can converse about almost any subject with a fluency that often surprises us.
Now that we understand what these Large Language Models are and how they have evolved, a natural question arises: how exactly can we use all this power in our daily lives? This is where the practical applications of these systems come in—and believe me, the possibilities are much broader and more revolutionary than you might imagine.
References Cited in This Section
[1] Hugo Touvron et al. "LLaMA: Open and Efficient Foundation Language Models". arXiv preprint arXiv:2302.13971, 2023. https://arxiv.org/abs/2302.13971
[2] Hugo Touvron et al. "LLaMA 2: Open Foundation and Fine-Tuned Chat Models". arXiv preprint arXiv:2307.09288, 2023. https://arxiv.org/abs/2307.09288
[3] Gemma Team et al. "Gemma: Open Models Based on Gemini Research and Technology". arXiv preprint arXiv:2403.08295, 2024. https://arxiv.org/abs/2403.08295
[4] Anthropic. "Claude 3 Model Card". Anthropic, 2024. https://www.anthropic.com/news/claude-3-family
[5] Ashish Vaswani et al. "Attention Is All You Need". Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017. https://arxiv.org/abs/1706.03762