Liquid AI releases LFM2.5-230M, a compact 230M-parameter model that outperforms larger rivals in data extraction
By
Carl Franzen
Summary
Liquid AI, founded by former MIT computer scientists, released LFM2.5-230M, a 230-million-parameter AI language model designed for on-device agentic workflows. Despite its small size, it outperforms models 4X larger on data extraction benchmarks and can run on smartphones, laptops, and robotics. The model is optimized for structured tool calls and keeping agentic pipelines running efficiently, positioning it as a practical alternative to larger models for enterprise data extraction tasks.
Source
bskyLiquid AI releases LFM2.5-230M, a compact 230M-parameter model that outperforms larger rivals in data extractionventurebeat.comKey quotes
· 4 pulledLiquid AI, founded by former MIT computer scientists, today released its smallest AI language model yet, LFM2.5-230M
That small size makes it possible to run nearly 'anywhere.'
It outperforms models more than 4X its size on selected benchmarks, specifically doing better at data extraction
A 230-million-parameter model is the superior, highly optimized choice for executing structured tool calls and keeping agentic pipelines running
You might also wanna read
LFM2: Liquid AI's Hybrid Edge AI Models for On-Device Deployment
LFM2 by Liquid AI is a new generation of hybrid models designed for on-device edge AI applications. The 1.5B parameter model is optimized fo
Product Hunt·6mo ago
GuppyLM: A 9M Parameter Language Model Demonstrating Accessible AI Training
The article introduces GuppyLM, a small 9-million parameter language model designed to demonstrate that training a language model is accessi
github.com·3mo ago
Fine-Tuned Small LLMs Outperform Larger Models at 5-30x Lower Cost with Data Curation
The article discusses how fine-tuned small language models (LLMs) can outperform larger ones at significantly lower costs (5-30x) through pr
tensorzero.com·11mo ago
LongCat-Flash: Meituan's 560B-Parameter MoE Language Model with Dynamic Computation and Open-Source Release
Meituan's LongCat Team introduces LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for computational
arxiv.org·7d ago
Liqvid AI Releases LFM2.5-8B-A1B: An Edge MoE Model for Tool Calling on Consumer Hardware
Liqvid AI released LFM2.5-8B-A1B, an edge-optimized Mixture-of-Experts model designed for fast, reliable tool calling on consumer hardware.
liquid.ai·1mo ago
MAKER System Solves Million-Step LLM Task with Zero Errors Through Extreme Decomposition
Researchers have developed MAKER, the first system to successfully solve a task requiring over one million LLM steps with zero errors, addre
arxiv.org·7mo ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.