Small Language Models (SLMs) vs LLMs: Smarter, Faster, Leaner AI | Squareboat Tech Talk

Name: Small Language Models (SLMs) vs LLMs: Smarter, Faster, Leaner AI | Squareboat Tech Talk
Uploaded: 2025-11-14T02:58:54.000Z
Duration: 32 min 27 s

Small Language Models: Making AI Lightweight and Efficient

Why Discuss Small Language Models (SLMs)?

The discussion focuses on the importance of small language models (SLMs) in making AI more efficient and cost-effective.

Running large models with billions of parameters for simple queries is inefficient, akin to using a supercomputer for basic calculations.

SLMs are needed to reduce computational costs, as larger models require significant hardware resources and specific environments for deployment.

Businesses need faster, cheaper, and private solutions that can be deployed on-premises rather than relying on external hardware.

What Are Small Language Models?

SLMs have fewer than 5 billion parameters compared to large language models (LLMs), which can have up to trillions of parameters.

They utilize the same transformer architecture as LLMs but are scaled down and trained on fewer parameters focused on specific domains like healthcare or business needs.

SLMs are faster, cheaper, and more energy-efficient for deployment, making them ideal for on-device applications.

How Do Small Language Models Work?

The architecture consists of four layers: transformer layer, distillation layer, quantization layer, followed by pruning and output layers.

A comparison shows that while LLM training data is vast and general-purpose, SLM training data is smaller and domain-specific.

Infrastructure requirements differ significantly; LLMs need GPUs/TPUs with large memory clusters while SLMs can run on modest hardware.

Capabilities of Small vs. Large Language Models

While SLM capabilities are constrained due to limited training datasets focused on specific purposes, LLM capabilities cover broader knowledge areas.

Customization is easier with SLM since they are simpler to fine-tune compared to complex LLM architectures.

Architecture Breakdown of Small Language Models

Input encoding converts words into numbers through position encoding that helps the model understand sequence order.

Self-attention mechanisms identify the main subject of user queries before passing it through a feed-forward neural network for answer generation.

Output normalization ensures the generated response is presented correctly after processing through various layers.

Techniques Used in Building Small Language Models

Key techniques include distillation (mimicking behavior from larger models), quantization (reducing parameter size), and pruning (removing unnecessary parts).

Distillation allows smaller models to learn from larger ones without needing all their knowledge datasets.

Quantization reduces storage size by converting tokens into smaller bit representations while sacrificing some precision.

What Techniques Improve Small Language Models?

Fine-Tuning and Pruning Techniques

Fine-tuning is a technique used to enhance the performance of small language models (SLMs) by adjusting parameters for better answers.

Pruning involves removing unnecessary weights from the model, retaining only critical attention heads to streamline processing and improve efficiency.

Microsoft’s 53 Mini is highlighted as an effective SLM with approximately 3.8 billion parameters, outperforming larger models in specific tasks.

Google JEM JEMA focuses on research and inference using ethical high-quality datasets, making it developer-friendly and open-source.

Tiny Llama, with 1.1 billion parameters trained on one trillion tokens, demonstrates extreme efficiency and is fully open-source.

Challenges Faced by Small Language Models

Despite their advantages, SLMs struggle with multi-step abstraction and reasoning compared to larger models like ChatGPT.

The reduction in precision due to fewer parameters limits the model's ability to provide accurate context or knowledge when answering queries.

Training small language models can lead to hallucinations where they generate incorrect responses not present in the training dataset.

Validation loss must be managed carefully; reducing it can help mitigate hallucination issues but may require task-specific dependencies for accuracy.

Evaluation challenges arise as SLM usefulness cannot always be measured against accuracy; they excel at basic NLP tasks but have inherent limitations.

Demonstration of a Small Language Model

A demo showcases a small language model trained on disease symptoms, utilizing data from Hugging Face for practical applications.

Although capable of generating responses for diseases not present in its training data, the issue of hallucination persists within generated outputs.

Future improvements could involve expanding the dataset significantly or increasing training cycles for enhanced accuracy without hardware limitations.

Data Preparation Steps

Initial steps include installing necessary dependencies such as Pandas and TensorFlow before loading the disease dataset from Hugging Face.

The process involves mapping words into numerical representations rather than treating them as full strings during tokenization for better understanding by the model.

Finalizing Data Setup

After preparing data sets, unique records are counted to ensure proper representation within the database while extracting relevant labels for training purposes.

The training size is set at 0.8 with batch sizes defined clearly; this structure aids in managing learning rates effectively during model training sessions.

Training Loss and Validation Process

Overview of Training and Validation Loops

The training loop involves training the model on provided input data, while the validation loop checks if the model's output is correct based on that input.

The model is trained using a specific dataset, with parameters like batch size and number of cycles being crucial for performance.

Observations on Training Loss

A significant reduction in training loss was noted, decreasing from 0.8 to 0.612 over eight cycles, indicating improved model performance.

However, after certain iterations, the model begins to hallucinate or produce incorrect outputs despite reduced training loss. This suggests potential issues with overfitting or underfitting.

Graphical Analysis of Loss

A graph comparing training loss versus validation loss shows a low training loss but a much higher validation loss, indicating a gap that suggests overfitting issues within the model.

The large gap between these losses highlights challenges in finding an optimal fit for both datasets during training processes involving SLMs (Statistical Language Models).

Techniques to Address Overfitting

Various techniques can be implemented to mitigate overfitting and improve generalization in models as discussed in this section. Suggestions include adjusting batch sizes or implementing regularization methods like dropout layers.

Model Querying and Limitations

Input String Mapping

The model has been queried with specific input strings related to depression symptoms, mapping words to numerical token IDs for processing within the system.

Output Decoding Challenges

While the decoded output aligns correctly with known symptoms such as anxiety and sweating, it may still hallucinate when asked about information not present in its database, highlighting limitations in its knowledge base and response accuracy.