What is a language model?

To be short yet precise, language models are derived from the study of maths, natural language processing, and deep learning. They are combined with huge amounts of data and increased computing capabilities of the modern age to derive a model that can generalize and answer user inputs based on its knowledge base, that is, its training data (at this point, almost everything available on the internet)

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LLM and SLMs:

Large Language and small language models are basically neural networks; they differ in their size or parameters (which depend on the computing resources while implementing the architecture and training). LLMs are huge and have more scope to generalize, while SLMs are compact, efficient, and typically built for more targeted tasks. Generally, SLM has parameters from the range of millions to lower billions, while LLM has parameters from the range of 100 billion to trillions 

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Some examples:

 Large Language Models (LLMs)

• GPT-4 (OpenAI) – Multimodal, high reasoning capabilities, ~1T+ parameters (exact count undisclosed)
• GPT-3.5 (OpenAI) – Fast and optimized for chat, ~175B parameters.
• LLaMA 2 70B (Meta) – Open-source, strong performance on reasoning tasks, 70B parameters.
• Claude 3 Opus (Anthropic) – Long context understanding and safe alignment-focused, ~200B+ parameters (estimated).
• PaLM 2 (Google) – Multilingual and code-capable transformer model, parameter range ~70B–540B.
• Gemini Ultra (Google DeepMind) – Advanced multimodal reasoning with a large context window, parameters in the hundreds of billions.
  
  

Small Language Models (SLMs)

• LLaMA 2 7B (Meta) – Lightweight, fine-tunable for custom tasks, 7B parameters.
• Mistral 7B (Mistral AI) – Highly efficient with sliding-window attention, 7B parameters.
• GPT-2 Small (OpenAI) – Legacy lightweight model for basic text tasks, 124M parameters.
• Phi-2 (Microsoft) – Compact with strong reasoning for its size, 2.7B parameters.
• TinyLlama 1.1B – Micro-scale transformer for edge deployments, 1.1B parameters.
• DistilBERT (Hugging Face) – Compressed BERT for classification tasks, ~66M parameters.

Pros and Cons : 

LLMs  The Heavyweight Champions

Why they shine:

• Built for deep reasoning, creative generation, and multi-step thought processes.
• Can understand long context windows, making them ideal for document-heavy workflows.
• Highly generalized, often performing well even without task-specific fine-tuning.
• Benefit from advanced alignment layers and safety training, reducing harmful or biased responses.
  
  

Where they struggle:

• Compute-hungry applications need dedicated GPUs and significant memory to run efficiently.
• Higher latency is not the best fit for real-time or on-device tasks.
• Cost scales quickly, especially in production-grade API usage.
• Sometimes overkill for narrow, rule-based tasks that don't require deep reasoning.
  
  

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SLMs  The Nimble Specialists

Why they shine:

• Lightweight and blazing fast, making them ideal for edge devices and low-latency applications, and also where data security is a priority.
• Can run on CPUs or small GPUs, unlocking private/on-prem deployments.
• Easier to fine-tune on custom datasets, allowing tight domain specialization.
• Cost-efficient both in terms of API billing and infrastructure.
  
  

Where they struggle:

• Limited reasoning depth, especially on complex or abstract prompts.
• Shorter context windows restrict them in tasks involving long documents.
• May require more prompt engineering or external logic (like RAG or rules) to stay accurate.
• Typically lack advanced safety tuning, which can be risky in user-facing systems.

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Common real-world use cases:

LLMs:

LLMs are ideal when the task demands depth, reasoning, creativity, or open-ended conversation.

• Complex reasoning and decision support generating insights, strategy suggestions, or multi-step explanations.
• Long-form content generation articles, reports, marketing copy, story writing, and code documentation.
• Multimodal understanding (in newer LLMs) interprets text, images, and context together.
• Exploratory chat and brainstorming, where the user may not even know the exact query upfront.
• Advanced code generation and refactoring where logic understanding is key.
• Multi-hop question answering queries that require chaining multiple knowledge points.
  
  

Think of LLMs as generalist consultants, broad, powerful, and great for exploration and creative problem-solving.

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SLMs:

SLMs are perfect for focused, repetitive, or rule-based tasks that prioritize speed, cost-efficiency, and control.

• Intent detection and classification tagging support tickets, routing customer queries.
• Entity extraction and form parsing, pulling out names, IDs, amounts, and dates from structured or semi-structured text.
• Sentiment analysis and tone detection at scale, with ultra-low latency.
• FAQ-style response generation with controlled outputs for chatbots.
• Inference at the edge or on-prem for privacy-first or offline use cases.
• Template-based content filling product descriptions, micro-copy, and email subject lines.
• Real-time translations or short summarizations where speed outweighs depth.
  
  

SLMs behave like skilled specialists, fast, reliable, and highly efficient at doing one thing extremely well.

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Practical implementation:

At Lotus Labs, we were tackling two very different problems, both under the umbrella of “AI chatbots” but they could not have been more distinct in what they demanded from the technology.

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For our QA chatbot, the mission was clear: “Don’t hallucinate. Don’t overthink. Just fetch the right piece of information from our product manual and respond clearly.” The knowledge base was static and structured as a goldmine of precise information that didn’t need creative interpretation, just accurate retrieval.

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Instead of deploying a heavyweight model that tries to “know everything,” we chose the smarter route: an SLM-based pipeline using a sentence-transformer for embeddings. The chatbot first retrieved the most relevant chunks using semantic similarity, and only then did a lightweight generative AI layer craft a clean, human-like response. Fast, cost-effective, and hyper-focused, exactly what a documentation assistant needs to be.

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The Text-to-SQL chatbot, however, lived in a different universe. Here, the system had to understand natural language, interpret intent, map it to relational database logic, and then generate syntactically valid SQL queries, all while reasoning about joins, filters, and context awareness. This wasn’t retrieval—this was a structured generation with logical constraints.

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That’s why we turned to a Large Language Model. For this use case, raw computational intelligence mattered the ability to reason, infer, and construct qualities that an SLM, no matter how optimized, isn’t meant to handle at that complexity.

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Check out the relevant links below for the solutions mentioned in the above few sentences

• Video analytics platform (Padme): https://www.padme.ai/
• QA chatbot for Padme: https://huggingface.co/spaces/sram77/ll-padme-chatbot (common questions: how to add, edit camera ?, how to set up people counting for my camera ?, etc.)
• Text 2 SQL chatbot (Petalytics): https://petalytics.co/
• What is Petalytics? -  blog link: https://www.lotuslabs.ai/post/why-chat-with-your-data-usually-disappoints----and-how-to-make-it-enterprise-grade

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Closing Remarks:

In AI, the goal isn’t to deploy the biggest model; it’s to deploy the smartest solution.

Sometimes that means unleashing a powerful LLM to reason and generate complex logic. Other times, it means letting a lean SLM whisper the right answer with speed, precision, and efficiency.

The real intelligence lies in choosing the right tool, not the heaviest one.

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