This article presents a concise overview of the top LLM (Large Language Model) choices that are optimized to run efficiently on Raspberry Pi. In recent times, advancements in quantization and model optimization have made it possible to harness the power of language models on small hardware like the Raspberry Pi.
We will explore five carefully selected LLM models that are not only compatible with Raspberry Pi’s 4GB RAM variant but also offer impressive capabilities despite hardware limitations. Each model will be accompanied by a brief description highlighting its unique features, dataset training, and quantization sizes.
By the end of this article, readers will gain valuable insights into the best LLM options for leveraging sophisticated natural language processing on the Raspberry Pi platform.
Note: I am not guaranteeing that it will work on your RPI, but I know from people’s experiences which kind of model with what kind of quantization might work on RPI. So, I collected the smallest but most capable ones, but it’s always worth a try.
LLM Models for Raspberry Pi
By running LLMs on Raspberry Pi, users can unlock a world of possibilities. They can create truly intelligent assistants with advanced natural language understanding, personalized chatbots for specific applications, engaging story-telling experiences, language translation tools, and even AI-powered educational aids. With the LLM’s enhanced reasoning and comprehension abilities, the potential for innovative and impactful applications is limitless.
BTLM-3B-8K
BTLM-3B-8K (Bittensor Language Model) is a powerful language model boasting 3 billion parameters and an 8k context length, making it suitable for various natural language processing tasks. Trained on an extensive dataset of 627 billion tokens from SlimPajama, BTLM-3B-8K sets a new standard for 3 billion parameter models, remarkably outperforming models trained on significantly larger token datasets, even rivaling the performance of open 7 billion parameter models.
One of the most impressive features of BTLM-3B-8K is its ability to be quantized to just 4 bits, enabling it to run on resource-constrained devices with as little as 3GB of memory. This optimization makes it a perfect candidate for deployment on small hardware platforms like the Raspberry Pi with 4GB RAM, bringing advanced language processing capabilities to edge devices.
Notably, BTLM-3B-8K comes with an Apache 2.0 license, allowing for commercial use and promoting its integration into various applications, from chatbots and language translation to text generation and more. While running the model on Raspberry Pi may not yield the fastest performance, its adaptability and efficiency are remarkable breakthroughs for making complex language models accessible on lightweight hardware.
Metharme_1.3B
Metharme 1.3B is a cutting-edge LLM model, leveraging the foundations of EleutherAI’s Pythia 1.4B Deduped. Designed with a focus on enhanced usability for conversation, roleplaying, and storywriting, Metharme offers an exceptional feature – natural language guidance. This means users can effortlessly guide the model through various tasks using ordinary language, similar to other instruct models.
Incorporating a unique training approach, Metharme underwent supervised fine-tuning, encompassing a diverse dataset that includes regular instructions, roleplay scenarios, fictional narratives, and synthetically generated conversations. This diverse training regimen results in a well-rounded, versatile LLM capable of handling various creative tasks.
Weighing in at approximately 3GB, Metharme’s efficient model size makes it compatible with consumer hardware, like the Raspberry Pi (4GB RAM recommended). Although its performance might not be lightning-fast, the model’s ability to run on such modest hardware is a testament to the advancements in quantization and model optimization.
StableBeluga 7B
StableBeluga 7B is a highly capable LLM model that can run on a Raspberry Pi, showcasing the remarkable advancements in quantization and model optimization. With a minimum quantization size of around 3 GB, it stands out as one of the most efficient choices for small hardware setups.
Trained on an orca style dataset, StableBeluga 7B exhibits enhanced reasoning capabilities, surpassing typical LLM models. Although it might not provide lightning-fast performance on the Raspberry Pi, its compatibility with the hardware makes it an excellent option for various applications where resource constraints are a concern. With StableBeluga 7B, users can harness the power of modern language models, even on the modest computing power of a Raspberry Pi.
Orca Mini v2 7B
Orca Mini v2 7B is an Uncensored LLaMA-7B model, developed in collaboration with Eric Hartford, tailored for Raspberry Pi usage. Trained on explain-tuned datasets, it incorporates insights from Instructions and Input datasets like WizardLM, Alpaca, and Dolly-V2, while adopting Orca Research Paper’s dataset construction approaches. With a quantization size starting at approximately 3 GB, this model demonstrates its adaptability to small hardware like Raspberry Pi. However, it’s important to note that quantization may limit its full potential, and the unquantized version might deliver significantly better performance in various tasks. Despite the trade-offs, Orca Mini v2 7B remains a promising choice for users seeking a resource-efficient language model suitable for Raspberry Pi applications.
