Ever wished your phone was a bit more, well, brainy? You’re not alone. Powerful AI called large language models are behind all sorts of cool stuff, from writing like Shakespeare to translating languages in a snap. But these brainiacs need serious muscle to run, usually found in fancy computers, not exactly pocket-friendly.
This article explores the idea of LLMs on regular phones, the kind you take everywhere. Imagine a super-powered assistant that answers your questions in detail, translates signs on vacation, or even writes you a poem – all on your trusty device. We’ll crack open the tech side of things to see what makes LLMs tick and how we might squeeze them into your phone. Get ready for a glimpse into the future of AI, where smarts fit right in your pocket.
LLM For Low End Smartphone
Phi-3-Mini-128K-Instruct
- RAM: 4 – 6 GB
Phi-3-Mini-128K-Instruct, a 3.8 billion-parameter open model that’s surprisingly lightweight and efficient.
What sets this model apart is its massive 128K context length, which is much bigger than most models out there. And despite its size, it can hold its own against models with 100B parameters! The Phi-3-Mini-128K-Instruct is trained on a unique dataset that combines synthetic data with high-quality, reasoning-dense website data, making it a beast when it comes to understanding and responding to complex queries.
However, be warned: this model is highly censored, so if your questions have even a hint of negative connotation, it might refuse to answer. It’s like it has a super strict moderator built-in! Google and Microsoft could learn a thing or two from Meta about fine-tuning models without being overly censored. Meta’s models genuinely have personality, and it’s something to aspire to.
To run this model smoothly, you’ll need at least 4-8 GB of RAM. But trust us, it’s worth it. The Phi-3-Mini-128K-Instruct is a game-changer for low-end smartphones, and we’re stoked to have it on our list!
phi-2-orange-v2
- RAM: 2 – 3 GB
Next up on our list of best LLMs for low-end smartphones is the Phi-2 Orange Version 2. This isn’t entirely new – it’s actually an improvement on the original Phi-2 Orange. Think of it like a remix with some extra spice.
Here’s the gist: they took the latest version of Microsoft’s Phi-2 model and fine-tuned it in two steps, using the same datasets as before. This process might sound technical, but the result is an LLM that punches above its weight.
With less than 3 billion parameters, it’s considered a lightweight model. But don’t let the size fool you. When paired with the right technique called quantization, this LLM can potentially run on smartphones with just 3GB of RAM. That’s impressive for a phone-based AI assistant or chatbot!
aanaphi2-v0.1
- RAM: 2 – 3 GB
Next up, we have aanaphi2-v0.1, a fine-tuned chat model that packs a punch despite its small size. Based on Microsoft’s Phi-2 base model, this LLM boasts 2.8 billion parameters and is optimized for low-end smartphones.
In benchmarks, aanaphi2-v0.1 impresses, offering performance that rivals models with larger parameter counts. Its compact size means it can also run efficiently on devices with just 3GB of RAM, making it an ideal choice for those with older or budget smartphones.
So, if you’re looking for an LLM that can provide a smooth and responsive experience on your low-end device, aanaphi2-v0.1 is a top contender. It proves that you don’t always need billions and billions of parameters to get great performance.
gemma-1.1-2b-it
- RAM: 2 – 3 GB
Gemma-1.1-2b-it is a standout option for those seeking an efficient and capable LLM for their low-end smartphones. With just under 2 billion parameters, Gemma 1.1 packs a punch despite its small size. Trained using a novel RLHF method, it boasts impressive performance across the board, including enhanced quality, coding capabilities, factual accuracy, instruction following, and multi-turn conversation fluidity.
What sets Gemma apart is its ability to run smoothly on devices with as little as 2GB or 3GB of RAM with the right quantization. This makes it an ideal choice for users who want the power of an LLM without the need for a high-end device.
So, if you’re looking for an LLM companion that won’t slow you down, Gemma-1.1-2b-it is a top contender and a worthy addition to our list.
stablelm-2-zephyr-1_6b
- RAM: 1 – 2 GB
Another impressive LLM that deserves a mention is Stable LM 2 Zephyr 1.6B. With a parameter count of just 1.6 billion, it punches above its weight. Based on HugginFaceH4’s Zephyr 7B training pipeline, this model benefits from a unique training approach.
What sets Stable LM 2 Zephyr 1.6B apart is its training data. By utilizing a mix of publicly available datasets and synthetic data, with Direct Preference Optimization (DPO), the model achieves impressive benchmark results.
Despite its small parameter size, this LLM is a top performer. With the right quantization, it can even run on smartphones with as little as 1GB of RAM, making it extremely accessible.
So, if you’re looking for an efficient and powerful LLM that won’t break the bank in terms of hardware requirements, Stable LM 2 Zephyr 1.6B is a fantastic option. It’s a testament to the advancements in LLM technology, proving that size isn’t everything!”
phi-1_5_chat
- RAM: 1 – 2 GB
And now, another strong contender for the best LLM for low-end smartphones is phi-1.5_chat.
With a name like phi-1.5, you might expect an upgrade from the previous version, and that’s exactly what you get. This Transformer model boasts 1.3 billion parameters, trained on a diverse dataset including Microsoft Research’s original phi-1 sources, plus a new batch of NLP synthetic texts.
What sets phi-1.5 apart is its impressive performance in benchmarks testing common sense, language understanding, and logical reasoning. It holds its own against models with up to 10 billion parameters, which is a significant achievement for an LLM of its size.
The beauty of phi-1.5_chat lies in its optimization for multi-turn conversations. By training the model with specific datasets, the developers have ensured that it excels in back-and-forth interactions, making it perfect for chat-based applications on smartphones.
But what about those with older devices? Well, the model’s relatively small size (by LLM standards) means that with the right quantization techniques, it can run smoothly on phones with as little as 1GB of RAM. That’s right, you don’t need a flagship device to access state-of-the-art language processing!
OpenHermes-Qwen1.5-1.8B
- RAM: 2 – 3 GB
Moving on to OpenHermes-Qwen1.5-1.8B, a great option for those with low-end smartphones. This underdog packs a punch despite having less than 2 billion parameters, making it efficient for devices with limited RAM. Here’s why it might be a good fit for you:
- Generalist Assistant: Don’t be fooled by its size, though. This LLM is fine-tuned on the OpenHermes-2.5 dataset, which means it’s got the chops to handle a wide range of tasks like a capable generalist assistant.
- Runs on Low RAM: With the right quantization version, you can even get OpenHermes-Qwen1.5-1.8B running on a phone with only 2GB of RAM, or even 1GB in some cases! That’s impressive efficiency for an LLM.
Hercules-Mini-1.8B
- RAM: 1 – 3 GB
Next on our list of low-powered LLMs is the Hercules-Mini 1.8B. Clocking in at a mere 1.8 billion parameters, this little powerhouse punches above its weight.
Hercules-Mini is a versatile LLM that can handle math, coding, roleplay, and even general assistant tasks. It’s designed to run on low-end phones with potentially as little as 1GB of RAM thanks to quantization techniques.
