Top Small Language Models to Self-Host in 2024

With the growing demand for AI-driven applications, small language models (SLMs) have gained popularity as powerful yet lightweight solutions. Unlike large language models (LLMs), which require extensive computational resources, SLMs offer the ability to perform complex natural language processing (NLP) tasks without straining your hardware or budget. This guide covers some of the best self-hostable small language models, including their use cases, system requirements, and links to getting started.

Why Choose Small Language Models?

Small language models are efficient and versatile. They are perfect for small to medium-sized businesses, developers on a budget, or AI enthusiasts who want to integrate language models into their applications without relying on cloud services. SLMs are often lighter, faster, and, in many cases, easier to deploy than their larger counterparts, making them ideal for on-premises or personal hosting setups. This article on Forbes provides insight on the rise of popularity of small language models.

Key Small Language Models You Can Self-Host

In this guide, we’ll explore several popular small language models you can self-host. Each model has been selected for its efficiency, compatibility with different hosting setups, and versatility across various NLP tasks.

1. GPT-J

• Use Case: General-purpose NLP tasks, such as summarization, translation, and creative text generation.
• System Requirements: 16 GB RAM (for smaller instances), NVIDIA Tesla T4 or similar GPU recommended for smooth operation.
• Getting Started: You can download GPT-J via EleutherAI’s GitHub repository.
• Setup Guide: Follow the GPT-J documentation on Hugging Face to get started.
• Key Points: GPT-J offers quality text generation capabilities and is considered one of the best open-source alternatives to OpenAI’s GPT models. Suitable for creative writing and general content generation tasks.

2. DistilBERT

• Use Case: Text classification, question answering, named entity recognition, and summarization.
• System Requirements: 8 GB RAM is sufficient, and it runs smoothly on CPUs, making it highly accessible.
• Getting Started: Download the model from Hugging Face’s Model Hub.
• Setup Guide: Refer to the DistilBERT documentation for installation and use.
• Key Points: DistilBERT is a lighter and faster version of BERT, designed for streamlined NLP tasks. It maintains the effectiveness of BERT while being easier to self-host.

3. SMOLLM2

• Use Case: Light text generation, chatbots, text summarization, and other NLP tasks.
• System Requirements: 4–6 GB RAM, making it feasible for users without high-end hardware.
• Getting Started: Download SMOLLM2 from Hugging Face’s Model Hub.
• Setup Guide: TechBuzz Online’s SMOLLM2 Guide provides step-by-step instructions for setup and optimization, making SMOLLM2 a good choice for NLP newcomers.
• Key Points: Known for its small footprint and efficiency, SMOLLM2 is a popular choice for small-scale NLP tasks and is ideal for projects requiring quick deployment on minimal hardware.

4. ALBERT

• Use Case: Effective in classification tasks, sequence labeling, and lightweight text generation.
• System Requirements: 8–12 GB RAM, no GPU required for standard use cases.
• Getting Started: ALBERT is hosted on Hugging Face.
• Setup Guide: Access the ALBERT documentation on Hugging Face for installation and usage instructions.
• Key Points: ALBERT is optimized for reduced memory use and training efficiency, making it ideal for users who need BERT-like performance on a smaller scale.

5. MiniLM

• Use Case: Semantic similarity tasks, sentence encoding, and lightweight NLP.
• System Requirements: 6 GB RAM, making it well-suited for low-resource environments.
• Getting Started: MiniLM is available via Hugging Face.
• Setup Guide: Find detailed setup instructions in the MiniLM documentation.
• Key Points: MiniLM provides an efficient solution for text similarity and other simple NLP tasks and is compatible with small hardware environments.

6. GPT-NeoX-20B (smaller configurations)

• Use Case: Advanced text generation and summarization tasks.
• System Requirements: 24 GB VRAM recommended for GPU-based setups.
• Getting Started: Access GPT-NeoX via EleutherAI’s GitHub repository.
• Setup Guide: Visit the GPT-NeoX documentation for more information on setup and configurations.
• Key Points: GPT-NeoX-20B offers high-performance text generation, rivaling larger models with fewer resources, ideal for sophisticated language processing on a modest budget.

7. Reformer

• Use Case: Document summarization and long-sequence text generation tasks.
• System Requirements: 12–16 GB RAM, and GPU acceleration is highly recommended.
• Getting Started: Reformer can be found on GitHub.
• Setup Guide: Detailed setup and usage instructions are available in the Reformer documentation.
• Key Points: Reformer excels at processing long text sequences efficiently and is ideal for summarization applications where memory efficiency is essential.

Factors to Consider When Self-Hosting Small Language Models

When deciding to self-host a small language model, consider the following factors:

1. Hardware Compatibility: Ensure your hardware meets the model’s system requirements. Most small models are CPU-compatible, though some may benefit significantly from GPU acceleration.
2. Model Size vs. Task Complexity: Smaller models like MiniLM or SMOLLM2 are ideal for simpler tasks, while models like GPT-J or ALBERT are better suited for complex NLP applications.
3. Latency and Speed: Self-hosted models offer more control over latency, which can be beneficial in real-time applications like chatbots or personalized user interfaces.
4. Budget and Maintenance: Small models are typically more budget-friendly, but hosting locally requires monitoring and maintenance to ensure they perform reliably.
5. Scalability: If your application’s demands increase, you may need to scale up resources or consider more advanced configurations to maintain performance.

Summary and Additional Resources

This guide has covered some of the most popular and accessible small language models for self-hosting. Whether you’re looking for a model to power a chatbot, conduct document summarization, or perform semantic similarity analysis, small language models offer a practical, cost-effective solution.

For more insights into the latest trends in language models, check out this comprehensive article on the rise of small language models over large language models on Forbes.

Each of these models offers distinct benefits and use cases. Choose one that aligns with your project goals and resources and enjoy the flexibility that comes with self-hosted NLP models.

Let us know if we have missed any models that you would want us to add here! Also share any use case of these models in the comments section below, if you are self-hosting them.

Credit: Feature image generated using ChatGPT and DALL·E 3 | OpenAI