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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.
What is Antares?
Antares is a collection of open-weight security small language models crafted to detect vulnerabilities within large codebases. Featuring models such as Antares-350M and Antares-1B, these tools can be deployed locally or on-site, ensuring that proprietary source code remains secure while also reducing both inference expenses and runtime. The procedure starts with an outline of the vulnerability, which may include an advisory or a CWE category; from there, the model embarks on a detailed investigation similar to that of a human analyst, methodically looking for relevant code patterns, scrutinizing possible files, integrating new data, and adjusting its strategy when certain paths appear unproductive. This method allows the model to concentrate its resources on the files most likely to contain the identified flaws. In the end, Antares produces a prioritized list of source files that may be vulnerable, accompanied by a comprehensive trail of the exploration process that led to these conclusions, thereby simplifying the review and prioritization for teams. Furthermore, this functionality not only accelerates the vulnerability assessment process but also significantly strengthens the overall security framework of the development environment, fostering a culture of proactive security measures. Ultimately, organizations can benefit from improved efficiency and effectiveness in managing their code vulnerabilities.
Integrations Supported
Amazon Bedrock
ElevenLabs
Gemma 3
Gemma 4
Hugging Face
LEAP
Llama
Llama 3.2
Qwen3
Integrations Supported
API Availability
API Availability
Pricing Information
Free
Free Version
Free Trial Offered?
Pricing Information
Pricing not provided
Supported Platforms
SaaS
Android
iPhone
iPad
Supported Platforms
SaaS
On-Prem
Customer Service / Support
Web-Based Support
Customer Service / Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Online Training
Training Options
Documentation Hub
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
Company Facts
Organization Name
Cisco
Date Founded
1984
Company Location
United States
Company Website
blogs.cisco.com/ai/introducing-antares-the-most-efficient-open-weight-ai-models-for-vulnerability-localization
Categories and Features
AI Models
Not specified
Open-Weight AI Models
Not specified