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Average Ratings 0 Ratings

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ease
features
design
support

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Description

Liquid AI's LFM2.5 represents an advanced iteration of on-device AI foundation models, engineered to provide high-efficiency and performance for AI inference on edge devices like smartphones, laptops, vehicles, IoT systems, and embedded hardware without the need for cloud computing resources. This new version builds upon the earlier LFM2 framework by greatly enhancing the scale of pretraining and the stages of reinforcement learning, resulting in a suite of hybrid models that boast around 1.2 billion parameters while effectively balancing instruction adherence, reasoning skills, and multimodal functionalities for practical applications. The LFM2.5 series comprises various models including Base (for fine-tuning and personalization), Instruct (designed for general-purpose instruction), Japanese-optimized, Vision-Language, and Audio-Language variants, all meticulously crafted for rapid on-device inference even with stringent memory limitations. These models are also made available as open-weight options, facilitating deployment through platforms such as llama.cpp, MLX, vLLM, and ONNX, thus ensuring versatility for developers. With these enhancements, LFM2.5 positions itself as a robust solution for diverse AI-driven tasks in real-world environments.

Description

ONNX provides a standardized collection of operators that serve as the foundational elements for machine learning and deep learning models, along with a unified file format that allows AI developers to implement models across a range of frameworks, tools, runtimes, and compilers. You can create in your desired framework without being concerned about the implications for inference later on. With ONNX, you have the flexibility to integrate your chosen inference engine seamlessly with your preferred framework. Additionally, ONNX simplifies the process of leveraging hardware optimizations to enhance performance. By utilizing ONNX-compatible runtimes and libraries, you can achieve maximum efficiency across various hardware platforms. Moreover, our vibrant community flourishes within an open governance model that promotes transparency and inclusivity, inviting you to participate and make meaningful contributions. Engaging with this community not only helps you grow but also advances the collective knowledge and resources available to all.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Bedrock Yes 
Azure SQL Edge No 
Cirrascale No 
ElevenLabs Yes 
Flyte No 
Gemma 4 Yes 
Groq No 
Higson No 
Hugging Face Yes 
Intel Open Edge Platform No 
LEAP Yes 
LaunchX No 
Llama Yes 
Llama 3.2 Yes 
ML.NET No 
OpenVINO No 
Qualcomm AI Hub No 
Qualcomm Cloud AI SDK No 
Qwen3 Yes 
SiMa No 

Integrations

Amazon Bedrock No 
Azure SQL Edge Yes 
Cirrascale Yes 
ElevenLabs No 
Flyte Yes 
Gemma 4 No 
Groq Yes 
Higson Yes 
Hugging Face No 
Intel Open Edge Platform Yes 
LEAP No 
LaunchX Yes 
Llama No 
Llama 3.2 No 
ML.NET Yes 
OpenVINO Yes 
Qualcomm AI Hub Yes 
Qualcomm Cloud AI SDK Yes 
Qwen3 No 
SiMa Yes 

Pricing Details

Free
Free Trial Yes 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App Yes 
iPad App Yes 
Android App Yes 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Liquid AI

Founded

2023

Country

United States

Website

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

Vendor Details

Company Name

ONNX

Website

onnx.ai/

Product Features

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Alternatives

Alternatives

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