This year, large language models (LLMs) like OpenAI’s o1 have dominated the headlines, showcasing their remarkable capabilities in natural language understanding and generation. However, not every application requires the immense computational power or the hefty size of these behemoths. Enter small language models — compact, efficient, and tailored solutions for tasks that demand high performance on a budget of computational resources.
Small language models are designed to strike a balance between capability and efficiency. By optimizing model size and architecture, they offer lightweight solutions ideal for edge devices, resource-constrained environments, or applications requiring faster inference. From powering mobile applications to providing offline NLP functionalities, these models are reshaping the landscape of AI by making advanced language technologies more accessible.
In this blog, we’ll explore the top 13 small language models that deliver impressive results while staying compact. Whether you’re a developer looking for lightweight solutions or a researcher exploring efficient NLP, this list highlights models that prove that bigger isn’t always better. Let’s dive in and discover how small models are making a big impact!
Table of contents
What is a Small Language Model?
A small language model is a type of AI system designed to understand and generate human-like text, but with limited size and complexity compared to larger models. These models have fewer parameters, which reduces their computational requirements, making them faster and more cost-effective to use.
While small language models may lack the nuanced reasoning or broader contextual understanding of larger models, they are highly efficient for focused tasks such as text classification, chatbots, or summarization. They are particularly useful in scenarios where memory, processing power, or energy consumption is a concern, such as mobile applications or embedded systems.
Their smaller size can also make them easier to fine-tune for specific tasks or integrate into constrained environments. However, their performance may degrade when tasked with understanding complex queries or generating highly detailed and coherent responses.
If you want to know about Small Language Models in more detail, here is a resource for you: What are Small Language Models (SLMs)?
Top 13 Small Language Models (SmallLMs)
Let us now look at the top 13 small language models.
1. Llama 3.2
Llama 3.2 is a compact yet powerful language model designed to cater to various natural language processing tasks while maintaining efficiency and adaptability. This model is part of the Llama series, which emphasizes high performance combined with resource efficiency, making it suitable for applications requiring lower computational overhead without sacrificing accuracy.
Size of Parameters
Llama 3.2 comes in several parameter configurations, allowing users to select the version that best meets their needs. These configurations typically range from a lightweight version with 1,3 billion parameters for mobile and edge deployments to a more robust version with 13 billion parameters for server-side applications. This scalability ensures the model can handle tasks of varying complexity while remaining efficient.
Architecture
The LLaMA 3.2 architecture begins with token embeddings and employs Grouped Query Attention, incorporating Rotary Positional Embedding (RoPE) for enhanced context encoding. RMS normalization is applied before attention and feedforward operations, stabilizing learning. Feed Forward networks utilize SwiGLU activations for efficient non-linear transformations. The architecture includes multiple stacked layers (repeated NNN-times), concluding with an RMS norm, linear layer, and softmax for output probabilities. Thus streamlining design balances computational efficiency with state-of-the-art performance, optimized for large-scale language modeling tasks.
Availability
Llama 3.2 is an open-source language model, making it accessible to a wide audience. It includes a free tier that allows users to experiment with its capabilities without incurring costs. Additionally, it offers extended features and enterprise-level support through paid licensing, catering to both individual developers and organizations.
Also Read: 3 Ways to Run Llama 3.2 on Your Device
2. Microsoft Phi 3.5 Mini
Microsoft Phi 3.5 Mini is a compact version of the Phi language model series developed by Microsoft. Designed to balance efficiency and performance, it caters to scenarios requiring robust natural language understanding with limited computational resources. The model is part of Microsoft’s ongoing efforts to create versatile AI systems optimized for a wide range of applications, including chatbots, summarization, and code generation.
Size of Parameters
The Phi 3.5 Mini model comes in various parameter configurations to suit diverse needs. The smallest variant contains 1.3 billion parameters, offering lightweight deployment capabilities. Larger configurations, such as the 3 billion-parameter version, are available for applications demanding higher accuracy and more contextual depth. This scalability makes Phi 3.5 Mini a flexible choice for users with different resource constraints and performance requirements.
Architecture
The model architecture builds upon the Transformer framework, incorporating innovations from the Phi series. It features advanced attention mechanisms optimized for computational efficiency and memory usage. Researchers have employed techniques like layer sparsification and dynamic token reduction to enhance processing speed while maintaining the model’s ability to generate coherent and contextually relevant outputs. These enhancements make Phi 3.5 Mini well-suited for real-time applications.
Availability
Microsoft Phi 3.5 Mini is a proprietary model, integrated into Microsoft’s Azure AI services. While the model is not open-source, it offers a free tier for limited usage, making it accessible for developers and researchers exploring its capabilities. Commercial applications require subscription plans, providing scalability and support for enterprise-grade deployments.
3. T5
The T5 (Text-To-Text Transfer Transformer) model is a versatile language model introduced by Google Research. It is designed with a unified framework where all NLP tasks are framed as a text-to-text problem. This approach enables the model to handle a variety of tasks, such as translation, summarization, and question-answering, using a single architecture and training process.
Size of Parameters
T5 is available in various sizes, ranging from small to extra-large configurations. The smaller versions include models like T5-Small with 60 million parameters and T5-Base with 220 million parameters. Larger configurations, such as T5-Large and T5-3B, offer 770 million and 3 billion parameters, respectively, while T5-11B, the largest variant, boasts 11 billion parameters. This scalability allows T5 to cater to both resource-constrained environments and high-performance tasks.
Architecture
The architecture of T5 is based on the Transformer model, utilizing both encoder and decoder components. Its design emphasizes flexibility, as it reframes input and output for any task into a text sequence. Thus allowing T5 to excel in fine-tuning for diverse NLP applications. The model incorporates pre-training on a diverse dataset, using objectives like a modified span-based corruption task, which enhances its understanding of language and context.
Availability
T5 is open-source and freely available to the research and developer community under the Apache 2.0 license. Its implementation and pre-trained weights can be accessed through platforms like TensorFlow and Hugging Face’s Transformers library. This open access has facilitated widespread experimentation and adoption in the NLP domain.
4. Qwen-2
Qwen-2 is a small language model designed to provide efficient natural language processing capabilities with a focus on computational resource optimization. Developed with cutting-edge machine learning techniques, Qwen-2 demonstrates strong capabilities across text generation, classification, summarization, and other NLP tasks, making it suitable for applications in diverse domains. Its modular architecture and lightweight design make it ideal for developers seeking performance on constrained hardware.
Size of Parameters
Qwen-2 is available in multiple parameter configurations to cater to varied use cases. The smaller version, with approximately 3 billion parameters, is optimized for edge devices and environments with limited computational power. For more demanding applications, a mid-sized variant with 7 billion parameters offers a balance between performance and resource requirements. At the upper end, the 13 billion parameter version is designed for applications requiring higher accuracy and complex task-handling capabilities, competing with larger language models while maintaining efficiency.
Architecture
The architecture of Qwen-2 is based on an advanced Transformer model, employing state-of-the-art techniques like multi-head self-attention and feed-forward neural networks. It incorporates optimizations such as rotary positional embeddings and adaptive pre-normalization to enhance both inference speed and training stability. The architecture is highly modular, enabling scalability and compatibility with a range of pretraining and fine-tuning frameworks. These features ensure Qwen-2’s robustness and adaptability in real-world deployments.
Availability
Qwen-2 is open-source and freely available for use, with certain advanced features accessible through a subscription-based tier. This ensures that developers and organizations of all scales can access and integrate the model into their projects.
5. DistilBERT
DistilBERT is a smaller, faster, and lighter version of the widely popular BERT (Bidirectional Encoder Representations from Transformers) model. Developed by Hugging Face, DistilBERT retains much of BERT’s performance while being more computationally efficient. It achieves this by leveraging a process called knowledge distillation, wherein a smaller “student” model learns to mimic the behavior of a larger “teacher” model. The result is a model that is significantly smaller yet delivers comparable results on various natural language processing tasks.
Parameter Size
DistilBERT reduces the size of BERT by 40% while retaining 97% of its language understanding capabilities. The standard version of DistilBERT has approximately 66 million parameters compared to BERT-base’s 110 million. This reduction in size makes it highly suitable for applications requiring low-latency inference or deployment on resource-constrained devices. There are no additional variations with different sizes within DistilBERT itself, but it serves as a midpoint between compact and full-scale transformer models.
Architecture
DistilBERT retains the Transformer architecture but simplifies it by reducing the number of layers. It has six Transformer layers compared to the twelve layers in BERT-base, with each layer consisting of a multi-head self-attention mechanism and feed-forward networks. Additionally, the model employs sinusoidal positional encodings to handle word position and uses layer normalization to stabilize training. DistilBERT also benefits from techniques such as dynamic masking, which improves generalization during pretraining. Despite having fewer layers, it achieves competitive performance by being pretrained on the same corpus as BERT, using a combination of language modeling and distillation objectives.
Availability
DistilBERT is open-source and freely available on platforms like Hugging Face’s Transformers library. It supports various tasks, such as text classification, question answering, and named entity recognition, without the need for extensive computational resources, making it accessible to developers and researchers alike.
6. Gemma 2
Gemma 2 is a small language model designed for efficient natural language understanding and generation tasks. Tailored for applications requiring lower computational resources, Gemma 2 balances accuracy and speed, making it suitable for use cases such as chatbots, content summarization, and interactive tools. Despite its smaller size compared to large-scale models, it achieves competitive performance through optimized training and architecture.
Size of Parameters
Gemma 2 is available in multiple parameter sizes, catering to a range of computational and application needs. The smallest variant, with 125 million parameters, is designed for lightweight tasks and edge devices. A mid-range version, featuring 350 million parameters, is ideal for tasks requiring slightly higher accuracy while still maintaining efficiency. The largest configuration, at 1.2 billion parameters, provides a more robust understanding and generation capability, suited for moderately complex NLP tasks while remaining manageable in terms of hardware requirements.
Architecture
The architecture of Gemma 2 is a transformer-based model, following the attention mechanism that has become a cornerstone of modern NLP. It employs a streamlined version of the transformer block to reduce computational overhead. Innovations such as dynamic attention heads and layer normalization enhancements improve both speed and model accuracy. The smaller parameter variants use fewer layers and reduced embedding dimensions, allowing for rapid inference on devices with limited resources. These adaptations make Gemma 2 an optimal choice for deploying high-performing models in resource-constrained environments.
Availability
Gemma 2 is open-source, with a permissive license that encourages community contributions and customization. Additionally, a free tier is offered for experimentation and integration into personal projects, making it accessible to developers and researchers. For enterprise use, premium options with extended support are available.
7. TinyBERT
TinyBERT is a distilled version of BERT (Bidirectional Encoder Representations from Transformers), designed to reduce the computational complexity and memory footprint of the original BERT model while retaining comparable performance. Developed with knowledge distillation techniques, TinyBERT compresses the knowledge of larger BERT models into a smaller form, making it suitable for resource-constrained environments like mobile devices and edge computing. The model is particularly useful for natural language understanding tasks, including sentiment analysis, question answering, and text classification.
Size of Parameters
TinyBERT is available in multiple configurations to balance model size and performance. The smallest version consists of 4 transformer layers, each with 312 hidden units, amounting to approximately 14 million parameters. This configuration is ideal for lightweight applications with stringent memory and computational limitations. A slightly larger variant, with 6 transformer layers and 768 hidden units, contains about 66 million parameters, offering improved accuracy while remaining significantly smaller than the original BERT, which has 110 million parameters.
Architecture
The architecture of TinyBERT closely mirrors the transformer-based design of the original BERT, albeit with fewer layers and reduced dimensions for efficiency. Each transformer layer in TinyBERT consists of a multi-head self-attention mechanism, followed by a feed-forward neural network with layer normalization and residual connections. Knowledge distillation ensures that the smaller model inherits knowledge from the teacher model (typically BERT), focusing on mimicking the teacher’s predictions, intermediate representations, and attention distributions. This allows TinyBERT to achieve strong performance relative to its compact size.
AvailabilityTinyBERT is open-source and freely available under the Apache License 2.0. It can be accessed and integrated into workflows via platforms like Hugging Face Transformers, ensuring accessibility for developers and researchers without licensing constraints.
8. MiniLM
MiniLM, developed by Microsoft, is a compact and efficient language model designed to deliver high performance while requiring fewer computational resources. It is part of a family of models that focus on optimizing knowledge distillation techniques, making it suitable for scenarios where computational efficiency and speed are critical. By compressing the knowledge of larger transformer models into a smaller architecture, MiniLM achieves a balance between size and performance, making it a popular choice for tasks like natural language understanding and text generation.
Size of Parameters
MiniLM is available in several sizes to accommodate different use cases and resource constraints. The smallest models feature as few as 6 layers and 22 million parameters, providing a lightweight option for resource-constrained environments. Medium-sized configurations with 12 layers and 33 million parameters are commonly used for applications requiring a balance between speed and accuracy. The largest version of MiniLM includes 384 million parameters and 24 transformer layers, delivering robust performance closer to larger transformer models while maintaining a smaller memory footprint.
Architecture
MiniLM is based on the transformer architecture, with specific adaptations to make it more compact. It utilizes a deep self-attention mechanism similar to models like BERT but incorporates innovations in knowledge distillation to transfer the performance of a larger teacher model to the smaller MiniLM. This process involves minimizing the difference between the teacher’s attention distributions and MiniLM’s, as well as aligning their hidden states, which ensures that the smaller model retains a significant portion of the larger model’s knowledge. The architecture supports multi-head attention and feed-forward layers but optimizes these components for faster inference and reduced computational costs.
Availability
MiniLM is open-source and freely available through platforms like Hugging Face Transformers and GitHub. Its accessibility allows developers and researchers to integrate it into diverse applications without licensing restrictions, fostering widespread adoption.
9. MobileBERT
MobileBERT is a lightweight and efficient adaptation of the popular BERT (Bidirectional Encoder Representations from Transformers) model, designed specifically to enable natural language processing tasks on resource-constrained devices such as mobile phones and edge devices. The model was introduced as a way to balance computational efficiency with accuracy, ensuring that smaller devices could perform complex language understanding tasks without compromising performance significantly.
Size of Parameters
The MobileBERT model is remarkably compact compared to the original BERT. It features a smaller number of parameters while retaining the ability to deliver high-quality results. The size of the parameters varies depending on the variant, but the standard MobileBERT configuration consists of approximately 25 million parameters, a significant reduction from the original BERT model’s 110 million parameters. This reduction is achieved through a careful process of knowledge distillation and architectural optimization.
MobileBERT employs a teacher-student training framework where the teacher model is a fine-tuned version of BERT and the student model is the compact MobileBERT. This process ensures that MobileBERT retains much of the knowledge and performance of its larger counterpart while significantly reducing the number of parameters and computational overhead.
Architecture
The architecture of MobileBERT is tailored for efficiency while preserving the core principles of the transformer model. Unlike BERT, which relies on a multi-layer transformer encoder with large hidden sizes, MobileBERT uses a bottleneck structure to reduce complexity. It incorporates a smaller embedding size and employs inverted bottleneck layers, inspired by techniques in mobile neural networks like MobileNet.
MobileBERT also replaces the original BERT’s feed-forward layers with a quadruple feed-forward network that adds depth and ensures that sufficient representational capacity is retained despite the reduction in size. The model uses a 24-layer architecture with each layer featuring fewer parameters than the original BERT but maintaining a comparable level of accuracy through knowledge distillation.
Availability
MobileBERT is open-source and freely available for use, making it accessible to developers and researchers alike. The model can be integrated into applications without licensing restrictions, ensuring widespread adoption across various platforms, including mobile devices.
10. DistilGPT-2
DistilGPT-2 is a smaller and more efficient version of OpenAI’s GPT-2 model, developed to offer a lighter alternative for applications requiring lower computational resources. By leveraging knowledge distillation techniques, DistilGPT-2 retains most of GPT -2’s capabilities while significantly reducing its size. This makes it a practical choice for tasks like text generation, summarization, and conversational agents where performance and resource efficiency are critical.
Size of Parameters
DistilGPT-2 is designed with approximately half the number of parameters compared to its parent model, GPT-2. While GPT-2 itself has multiple variants ranging from 117M to 1.5B parameters, DistilGPT-2 typically corresponds to the 82M parameter range, striking a balance between performance and computational efficiency. This reduction is achieved without a substantial compromise in the model’s understanding or generation capabilities, owing to the knowledge distillation process.
Architecture
DistilGPT-2 maintains a similar architecture to GPT-2, built upon the Transformer model. It uses multi-head self-attention layers and feed-forward neural networks to process and generate text. However, to reduce its size and computational requirements, DistilGPT-2 cuts down on the number of layers while keeping the key structural elements intact. The underlying methodology involves training the smaller model to mimic the output distributions of the larger GPT-2, enabling it to generalize effectively with fewer parameters.
Availability
DistilGPT-2 is open-source and freely available through the Hugging Face model repository. Its accessibility, combined with its reduced size, makes it a popular choice for developers and researchers working on resource-constrained systems.
11. Mistral Nemo
Mistral Nemo is a compact and efficient language model. It was developed with a focus on delivering high-quality language understanding and generation capabilities while maintaining scalability and speed. Built to support diverse applications, it emphasizes efficiency in performance and ease of integration into various systems.
Size of Parameters
Mistral Nemo is available in multiple configurations, catering to a range of use cases. The model comes in sizes including 1.3 billion, 7 billion, and 13 billion parameters, allowing users to balance computational resource requirements with model complexity and performance. Each size variant is optimized for specific scenarios, from lightweight applications to those requiring deeper linguistic nuance.
Architecture
The architecture of Mistral Nemo is grounded in transformer-based design principles. Leveraging advancements in transformer models, Mistral Nemo incorporates innovations such as optimized attention mechanisms and enhanced token embeddings, ensuring efficient memory usage and computational throughput. The architecture is structured to maximize performance on both single-node and distributed setups, making it highly adaptable for diverse workloads.
Availability
Mistral Nemo is open-source, providing developers with free access to the model and its underlying codebase. This accessibility enables extensive customization and integration for various applications.
12. SmolLM
SmolLM is a lightweight language model designed to provide efficient natural language processing capabilities while maintaining a reduced computational footprint. Its development focuses on striking a balance between model performance and accessibility, making it ideal for applications where resource constraints are a primary concern. SmolLM is particularly suitable for edge devices, quick prototyping, and tasks that require low-latency responses.
Parameter Sizes
SmolLM is available in multiple configurations to accommodate different performance and resource needs. The smallest model contains approximately 10 million parameters, while mid-range versions include models with 50 million and 100 million parameters. For applications requiring slightly higher capacity without sacrificing speed, a 300-million-parameter variant is also offered. Each configuration is optimized for efficient inference, allowing for deployment on resource-constrained devices such as mobile phones and edge servers.
Architecture
The architecture of SmolLM is rooted in transformer-based designs, specifically tailored to reduce parameter redundancy without compromising performance. It employs advanced pruning and quantization techniques, alongside lightweight attention mechanisms, to achieve its compact form. Additionally, SmolLM integrates adaptive computation methods, enabling it to allocate resources dynamically based on task complexity. This design ensures that the model retains high accuracy and fluency in natural language tasks while maintaining efficiency.
Availability
SmolLM is open-source and available for download under a permissive license. A free tier for online use is also offered, with extended features accessible through a subscription plan.
13. Phi-4
Phi-4 is a 14-billion parameter language model developed by Microsoft Research. It is designed to excel in reasoning tasks while maintaining computational efficiency. This model builds on the Phi family of models and incorporates advanced techniques in data generation and refinement to deliver high performance on reasoning-focused tasks. Unlike many larger models, Phi-4 aims to strike a balance between capability and resource efficiency. Hence making it a practical tool for real-world applications.
Parameter Sizes
The Phi-4 model features 14 billion parameters. This is a deliberate choice that aligns with its focus on reasoning efficiency and reduced computational demands. This size is optimized to outperform larger models such as GPT-4 and Llama-3 in specific benchmarks. Therefore, showcasing the potential of compact architectures when paired with innovative training methodologies.
Architecture and Training
Phi-4’s architecture is tailored to enhance reasoning and problem-solving. Key elements of its training process include the use of synthetic data generated through multi-agent prompting and instruction reversal, which helps create datasets rich in structured, real-world scenarios. Post-training refinements, such as rejection sampling and Direct Preference Optimization (DPO), further improve the model’s logical consistency and usability. Additionally, the context length of the model was extended from 4,000 to 16,000 tokens during midtraining, enabling it to handle complex, long-chain reasoning tasks effectively.
Availability
Phi-4 is currently not open-source and remains a proprietary model. Details on access, including any free or limited-tier usage options, have not been disclosed, suggesting it is primarily positioned for specific research and enterprise applications.
Conclusion
Therefore, smallLMs are making significant strides in transforming the field of NLP by offering a balance between performance, efficiency, and accessibility. Unlike their larger counterparts, these models are designed to operate in resource-constrained environments. Thus making them ideal for mobile applications, edge devices, and scenarios requiring real-time responses. By leveraging advancements in model compression, knowledge distillation, and optimized architectures, small models prove that compactness does not necessarily mean a compromise in quality.
Moreover, the versatility of small language models is evident in their applications. They have the ability to power chatbots and summarization tools to enable offline NLP capabilities. Open-source models like T5, Qwen-2, and Mistral Nemo drive innovation by making advanced technology accessible to more people. Proprietary models like Microsoft Phi 3.5 Mini show how tailored solutions meet specific enterprise needs.
As AI demand rises across sectors, small language models will remain crucial for scaling NLP technologies efficiently and inclusively. These models prove that smaller, optimized architectures can achieve impressive results, bringing AI to new domains and users.
Frequently Asked Questions
Q1. Can small language models be used offline?
A. Yes, due to their lightweight nature, small language models can be deployed offline on devices like smartphones or embedded systems, depending on the application.
Q2. How are small language models fine-tuned?
A. Fine-tuning involves adjusting a pretrained model to improve its performance on a specific task using a smaller, task-specific dataset. This is done by continuing the training process with the new data.
Q3. Are small language models secure and private?
A. They can be more secure as they are often deployed locally, minimizing the need to send sensitive data over the internet. However, the level of security depends on the implementation.
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