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What is ONNX?

ONNX offers a standardized set of operators that form the essential components for both machine learning and deep learning models, complemented by a cohesive file format that enables AI developers to deploy models across multiple frameworks, tools, runtimes, and compilers. This allows you to build your models in any framework you prefer, without worrying about the future implications for inference. With ONNX, you can effortlessly connect your selected inference engine with your favorite framework, providing a seamless integration experience. Furthermore, ONNX makes it easier to utilize hardware optimizations for improved performance, ensuring that you can maximize efficiency through ONNX-compatible runtimes and libraries across different hardware systems. The active community surrounding ONNX thrives under an open governance structure that encourages transparency and inclusiveness, welcoming contributions from all members. Being part of this community not only fosters personal growth but also enriches the shared knowledge and resources that benefit every participant. By collaborating within this network, you can help drive innovation and collectively advance the field of AI.

What is LFM2.5?

Liquid AI's LFM2.5 marks a significant evolution in on-device AI foundation models, designed to optimize efficiency and performance for AI inference across edge devices, including smartphones, laptops, vehicles, IoT systems, and various embedded hardware, all while eliminating reliance on cloud computing. This upgraded version builds on the previous LFM2 framework by significantly increasing the scale of pretraining and enhancing the stages of reinforcement learning, leading to a collection of hybrid models that feature approximately 1.2 billion parameters and successfully balance adherence to instructions, reasoning capabilities, and multimodal functions for real-world applications. The LFM2.5 lineup includes various models, such as Base (for fine-tuning and personalization), Instruct (tailored for general-purpose instruction), Japanese-optimized, Vision-Language, and Audio-Language editions, all carefully designed for swift on-device inference, even under strict memory constraints. Additionally, these models are offered as open-weight alternatives, enabling easy deployment through platforms like llama.cpp, MLX, vLLM, and ONNX, which enhances flexibility for developers. With these advancements, LFM2.5 not only solidifies its position as a powerful solution for a wide range of AI-driven tasks but also demonstrates Liquid AI's commitment to pushing the boundaries of what is possible with on-device technology. The combination of scalability and versatility ensures that developers can harness the full potential of AI in practical, everyday scenarios.

Media

Media

Integrations Supported

Amazon Bedrock
Azure SQL Edge
Cirrascale
ElevenLabs
Flyte
Gemma 3
Gemma 4
Groq
Higson
Intel Open Edge Platform
LEAP
LaunchX
Llama
Llama 3.2
ML.NET
OpenVINO
Qualcomm AI Hub
Qualcomm Cloud AI SDK
Qwen3
SiMa

Integrations Supported

Amazon Bedrock
Azure SQL Edge
Cirrascale
ElevenLabs
Flyte
Gemma 3
Gemma 4
Groq
Higson
Intel Open Edge Platform
LEAP
LaunchX
Llama
Llama 3.2
ML.NET
OpenVINO
Qualcomm AI Hub
Qualcomm Cloud AI SDK
Qwen3
SiMa

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

ONNX

Company Website

onnx.ai/

Company Facts

Organization Name

Liquid AI

Date Founded

2023

Company Location

United States

Company Website

www.liquid.ai/blog/introducing-lfm2-5-the-next-generation-of-on-device-ai

Categories and Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Categories and Features

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