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What is LFM2?

LFM2 is a cutting-edge series of on-device foundation models specifically engineered to deliver an exceptionally fast generative-AI experience across a wide range of devices. It employs an innovative hybrid architecture that enables decoding and pre-filling speeds up to twice as fast as competing models, while also improving training efficiency by as much as threefold compared to earlier versions. Striking a perfect balance between quality, latency, and memory use, these models are ideally suited for embedded system applications, allowing for real-time, on-device AI capabilities in smartphones, laptops, vehicles, wearables, and many other platforms. This results in millisecond-level inference, enhanced device longevity, and complete data sovereignty for users. Available in three configurations with 0.35 billion, 0.7 billion, and 1.2 billion parameters, LFM2 demonstrates superior benchmark results compared to similarly sized models, excelling in knowledge recall, mathematical problem-solving, adherence to multilingual instructions, and conversational dialogue evaluations. With such impressive capabilities, LFM2 not only elevates the user experience but also establishes a new benchmark for on-device AI performance, paving the way for future advancements in the field.

What is Alpaca?

Models designed to follow instructions, such as GPT-3.5 (text-DaVinci-003), ChatGPT, Claude, and Bing Chat, have experienced remarkable improvements in their functionalities, resulting in a notable increase in their utilization by users in various personal and professional environments. While their rising popularity and integration into everyday activities is evident, these models still face significant challenges, including the potential to spread misleading information, perpetuate detrimental stereotypes, and utilize offensive language. Addressing these pressing concerns necessitates active engagement from researchers and academics to further investigate these models. However, the pursuit of research on instruction-following models in academic circles has been complicated by the lack of accessible alternatives to proprietary systems like OpenAI’s text-DaVinci-003. To bridge this divide, we are excited to share our findings on Alpaca, an instruction-following language model that has been fine-tuned from Meta’s LLaMA 7B model, as we aim to enhance the dialogue and advancements in this domain. By shedding light on Alpaca, we hope to foster a deeper understanding of instruction-following models while providing researchers with a more attainable resource for their studies and explorations. This initiative marks a significant stride toward improving the overall landscape of instruction-following technologies.

Media

Media

Integrations Supported

Hugging Face
OpenAI
OpenRouter
Qwen3
Together AI

Integrations Supported

BERT
ChatGPT
Dolly
GPT-4
Llama
Ludwig
Stable LM

API Availability

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Not specified

Company Facts

Organization Name

Liquid AI

Date Founded

2023

Company Location

United States

Company Website

www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models

Company Facts

Organization Name

Stanford Center for Research on Foundation Models (CRFM)

Company Location

United States

Company Website

crfm.stanford.edu/2023/03/13/alpaca.html

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

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