Small Language Models (SLMs) are small and more efficient versions of artificial intelligence (AI) models. They are designed to process and generate human-like texts with fewer computational resources than the large language models (LLMs). Here is a detailed guide for understanding SLMs.
What Are Small Language Models?
SLMs are AI models trained using text data to complete a variety of tasks, including text generation, text completion, translations, summarising, and answering questions. LLMs (such as GPT-4) can have billions or trillions of parameters. SLMs typically have millions or a few billion parameters, making them small, efficient, fast, and cost-effective for a narrower range of users. They are often run for local or personal purposes, which reduces the need for sharing confidential data and hence are more secure.
How are SLMs operated and optimised?
SLMs typically operate in a very similar manner to large language models (LLMs). They both have similar architecture. SLMs come with optimisations like:
- Model Compression: SLMs are the shrunk model size of the LLMs. It is done with techniques such as pruning (removing the unneeded connections or database in the models) or quantisation (reducing the numerical precision without affecting accuracy).
- Distillation: Train a smaller model to replicate the behaviour of a larger model so it retains the performance of the larger version.
- Training Improvement: By having a curated and high-quality training dataset, the training time is reduced. It generates a smaller model that can perform well even with less data.
The processed text, in general, is the language model predicting the next word or generating responses to something based on patterns learnt while training the model. SLMs are more targeted for specified tasks and not as general-purpose agents as large LLMs, but they can perform high-leverage tasks efficiently.
Examples of SLMs
- DistilBERT: a distilled version of BERT with 40% fewer parameters. It is intended for tasks such as text classification.
- TinyLlama: a smaller language model (e.g., 1.1B parameters) for research. It is optimised for cost-effective deployment.
- Grok(xAI): It is not always called an SLM; models like Grok are designed for efficiency in specific tasks.
- Phi Series from Microsoft: The smaller language model versions, like Phi1 and Phi2, are optimised for specified excellence in their domains, like coding and reasoning.
- Further SLMs were launched, including LaMini-GPT, MobileLLaMA, and TinyLlama, in 2024, which are designed to perform well on mobile devices and other low-powered systems. Companies such as Meta, Google, and Microsoft are driving the development of these models, with some available to the public and others kept private.
Benefits of SLMs
- Accessibility: They require less computational power and can run on consumer hardware or edge devices.
- Speedy Inference: They can generate fast inferences by having a lower engagement and response time due to fewer constraints in terms of computation.
- Cost Effective: They can result in less overall cost to train, use, and deploy.
- Customisable: More suitable for customisation via fine-tuning and domain settings. It is easier to fine-tune them with domain-specific data.
- Safer: SLMs are less prone to cyber attacks. They are trained on highly specific data, which eliminates the need for big connections outside the network and hence secures the critical data.
Drawbacks of SLMs
- Limited Knowledge: Smaller models have less capacity to store general knowledge. They are also built in a smaller size, constraining their generative capabilities, like in-depth analysis or sophisticated content creation.
- Narrow Task Scope: They can be very competent on specific tasks but tend to struggle on complex, open-ended queries.
- Performance trade-off: Smaller models tend to be less accurate and less fluent than LLMs, while being the most efficient (in terms of computing resources).
- Multilingual Limitations: Due to SLM's small size, their ability to encode the complexities of linguistic diversity, such as syntax, semantics, and cultural nuances across languages, is limited.
Applications and Uses of SLMs
Small Language Models (SLMs) offer various applications across domains. Here are some of the key examples where SLMs are invaluable:
- Chatbots: SLMs can be used to run customer service chatbots or virtual assistants on a mobile device.
- Edge AI: It can process language on edge devices with limited processing abilities (like smart speakers for language consumption).
- Domain-Specific Applications: Smaller modules can process particular types of documents, like medical, legal, or financial records.
- Education Tools: They can be used to power language apps that require minimal latency to support student learning or tutoring.
- IoT Devices: SLMs can process text or accept voice commands from a smart home device.
- Optical Character Recognition (OCR): SLMs improve OCR systems by accurately recognising and converting pictures of text into machine-encoded text, enabling document digitisation and automating data entry.
- Sentiment analysis: SLMs analyse text sentiment to assist organisations in evaluating public opinion, understanding consumer feedback, and making data-driven decisions to improve products, services, and brand reputation.
Why are SLMs Important?
SLMs create democratised opportunities for organisations, developers, and devices with limited computing resources to be able to process language. SLMs follow the trend of sustainable computing as they consume less energy than huge LLMs. As edge computing and issues surrounding privacy are rising, SLMs are becoming critical options for using AI in the real world and resource-restricted environments.
Starting with SLMs:
- Use Open-Sourced Models: Check out DistilBERT or TinyLlama on websites like Hugging Face.
- Experimentation Locally: Experimenting on your personal computer using Python and PyTorch to fine-tune SLMs.
- Learn Optimisation Techniques: Learn model compression or distillation for building your SLMs.
These are the general steps one can use to start learning and building their SLMs.
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Conclusion
SLMs are making AI accessible to everyone, like individual developers, small companies, and startups. They don’t need extensive resources like massive servers, computational power and budgets.
The effectiveness of these models is determined not just by their size but also by their ability to maintain performance metrics equal to larger competitors. As we continue to look into the potential of small language models, it is critical to prioritise their improvement to ensure they maintain efficiency while offering robust performance across a variety of activities and domains.
