SLMs (Small Language Models)

Extracted Attributes

• Type

  Artificial intelligence language model

• Scale

  Significantly smaller than Large Language Models (LLMs)

• Key Benefits

  Faster training, reduced energy consumption, lower latency, adaptability, more accessible, efficient, customizable, affordable, practical, suitable for specific tasks

• Primary Purpose

  Natural language processing, text generation, respond to and generate natural language

• Architectural Basis

  Often shares the same architecture as LLMs

• Parameter Count Range

  Typically a few thousand to a few hundred million parameters (some up to a few billion)

• Potential Limitations

  Limited capacity for complex language, reduced accuracy in complex tasks

• Outperformance Examples

  Diabetica-7B outperformed GPT-4 and Claude-3.5 in diabetes-related inquiries; Mistral 7B outperformed Meta's LLaMA 2 13B across various benchmarks

• Example Specialized Model

  Diabetica-7B (for diabetes-related inquiries)

• Size Reduction Techniques

  Knowledge distillation, pruning, quantization (lower arithmetic precision)

• Computational Requirements

  Less computational power, feasible for resource-constrained environments (single computer, mobile device, consumer hardware, edge devices)

• Example Models (1-4B parameters)

  Llama3.2-1B, Qwen2.5-1.5B, DeepSeek-R1-1.5B, SmolLM2-1.7B, SmolVLM-2.25B, Phi-3.5-Mini-3.8B, Phi-4-Mini-3.8B, Gemma3-4B, Gemini Nano

• Example Models (4-14B parameters)

  Mistral 7B, Gemma 9B, Phi-4 14B

• Example Models (Below 1B parameters)

  Llama-Prompt-Guard-2-22M, SmolLM2-135M, SmolLM2-360M

Timeline

• Most contemporary Small Language Models (SLMs) begin to use the same architecture as Large Language Models (LLMs), but with reduced parameter counts and sometimes lower arithmetic precision. (Source: Wikipedia)

  2020s

• A strategic shift is observed in the AI industry, moving away from monolithic Large Language Models (LLMs) towards more efficient Small Language Models (SLMs) and successful Vertical AI Applications. (Source: Related Document)

  Present

Small language model

Small language models or compact language models are artificial intelligence language models designed for human natural language processing including language and text generation. Unlike large language models, small language models are much smaller in scale and scope. Typically, an large language models's number of training parameters is in the hundreds of billions, with some models even exceeding a trillion parameters. The size of any large language model is vast because it contains a large amount of information, which allows it to generate better content. However, this requires enormous computational power, making it impossible for an individual to train a large language model using just a single computer and graphical processing unit. Small language models, on the other hand, use far fewer parameters, typically ranging from a few thousand to a few hundred million. This make them more feasible to train and host in resource-constrained environments such as a single computer or even a mobile device. Most contemporary (2020s) small language models use the same architecture as a large language model, but with a smaller parameter count and sometimes lower arithmetic precision. Parameter count is reduced by a combination of knowledge distillation and pruning. Precision can be reduced by quantization. Work on large language models mostly translate to small language models: pruning and quantization are also widely used to speed up large language models. Some notable models are: Below 1B parameters: Llama-Prompt-Guard-2-22M (detects prompt injection and jailbreaking, based on DeBERTa-xsmall), SmolLM2-135M, SmolLM2-360M 1–4B parameters: Llama3.2-1B, Qwen2.5-1.5B, DeepSeeek-R1-1.5B, SmolLM2-1.7B, SmolVLM-2.25B, Phi-3.5-Mini-3.8B, Phi-4-Mini-3.8B, Gemma3-4B; closed-weights ones include Gemini Nano 4–14B parameters: Mistral 7B, Gemma 9B, Phi-4 14B. (Phi-4 14B is marginally "small" at best, but Microsoft does market it as a small model.)

Web Search Results

• What Are Small Language Models (SLMs)? - Microsoft Azure

  Small language models (SLMs) are a subset of language models that perform specific tasks using fewer resources than larger models. SLMs are built with fewer parameters and simpler neural architectures than large language models (LLMs), allowing for faster training, reduced energy consumption, and deployment on devices with limited resources. Potential limitations of SLMs include a limited capacity for complex language and reduced accuracy in complex tasks. [...] A small language model (SLM) is a computational model that can respond to and generate natural language. SLMs are designed to perform some of the same natural language processing tasks as their larger, better-known large language model (LLM) counterparts, but on a smaller scale. They’re built with fewer parameters and simpler neural network architectures, which allows them to operate with less computational power while still providing valuable functionality in specialized applications. [...] OVERVIEW Overview How it works Benefits Challenges Types Use cases Future of SLMs Resources FAQ READ TIME 10 min An overview of small language models (SLMs) ------------------------------------------- Small language models (SLMs) are computational models that can respond to and generate natural language. SLMs are trained to perform specific tasks using fewer resources than larger models. ### Key takeaways

• Small language models vs. large language models

  # What is a small language model? Small language models (SLMs) are smaller models with significantly fewer parameters than LLMs, typically millions to a few billion rather than tens or hundreds of billions. These models run efficiently on consumer hardware, including laptops, smartphones, and edge devices. [...] Small language models (SLMs) emerge as an alternative to LLMs, which have shown exceptional performance in specialized domains, such as healthcare. For instance, the Diabetica-7B model, designed for diabetes-related inquiries, achieved an accuracy rate of 87.2%, surpassing GPT -4 and Claude-3.5. Similarly, Mistral 7B, a popular SLM with 7 billion parameters, has been reported to outperform Meta's LLaMA 2 13B across various benchmarks. [...] LLMs, like GPT-3, which powers OpenAI’s ChatGPT, are generative AI models trained on internet-scale data, excelling at general-purpose text generation and natural language understanding. In contrast, SLMs—compact models fine-tuned for specific workflows—provide targeted solutions.

• What are Small Language Models (SLMs)? - Aisera

  Small Language Models (SLMs) represent a specialized subset within the broader domain of artificial intelligence, specifically tailored for Natural Language Processing (NLP). SLMs are Characterized by their compact architecture and less computational power. Small Language Models are engineered to efficiently perform specific language tasks, with a degree of efficiency and specificity that distinguishes them from their Large Language Model (LLM) counterparts. [...] As businesses continue to navigate the complexities of generative AI, Small Language Models are emerging as a promising solution that balances capability with practicality. They represent a key development in AI’s evolution and offer enterprises the ability to harness the power of AI in a more controlled, efficient, and tailored manner. [...] 4- Adaptability and Lower Latency Small Language Models offer a degree of adaptability and responsiveness that is crucial for real-time applications. Their smaller size allows for lower latency in processing requests, making them ideal for AI customer service, real-time data analysis, and other applications where speed is of the essence. Furthermore, their adaptability facilitates easier and quicker updates to model training, ensuring that the SLM remains effective over time.

• Small Language Models (SLMs) [2024 overview] - SuperAnnotate

  Small language models (SLMs) are AI models designed to process and generate human language. They're called "small" because they have a relatively small number of parameters compared to large language models (LLMs) like GPT-3. This makes them lighter, more efficient, and more convenient for apps that don't have a ton of computing power or memory. [...] To sum it up, small language models are making a big impact in AI. They're affordable, practical, and fit well into many business needs without the need for supercomputers. These models are great for a range of tasks—from customer service to number crunching and even educational applications—all without the heavy resource use that bigger models often require.As technology progresses, small language models are only going to become more important. They give businesses of all sizes a more [...] The best thing about small language models (SLMs) is that they work great even on simpler hardware, which means you can use them in lots of different settings. They're perfect if you don't need all the fancy features of a huge language model. Plus, you can fine-tune SLMs to do exactly what you need, making them really good for specific tasks. If your business is starting to play around with GenAI, SLMs can be set up quickly and easily. How does an SLM work?

• Small Language Models: A Guide With Examples - DataCamp

  Small language models are the compact, highly efficient versions of the massive large language models we’ve heard so much about. LLMs like GPT-4o have hundreds of billions of parameters, but SMLs use far fewer—typically in the millions to a few billion. The key characteristics of SLMs are: [...] Get your team access to the full DataCamp for business platform.For BusinessFor a bespoke solution book a demo. Small language models (SLMs) solve the problem of making AI more accessible and efficient for those with limited resources by being smaller, faster, and more easily customized than large language models (LLMs).