LFM2.5 2.6B

Name: LFM2.5 2.6B
Author: Liquid AI

by Liquid AILFM2.5

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released November 2025131K context≈ 98,304 words2.6B params

A compact model that punches above its weight class for its parameter count, handling long contexts up to 128K tokens — unusual for something this small. It's a text-in, text-out workhorse built on LiquidAI's LFM2.5 architecture, which differs from standard transformer designs. Expect efficient inference with reasonable capability, though detailed public benchmarking on specific tasks remains limited.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Moderate

Creative Writing

LFM2.5 2.6B

by Liquid AILFM2.5

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released November 2025131K context≈ 98,304 words2.6B params

A compact model that punches above its weight class for its parameter count, handling long contexts up to 128K tokens — unusual for something this small. It's a text-in, text-out workhorse built on LiquidAI's LFM2.5 architecture, which differs from standard transformer designs. Expect efficient inference with reasonable capability, though detailed public benchmarking on specific tasks remains limited.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Moderate

Creative Writing

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Compact ModelA smaller language model designed to use fewer computational resources while still performing useful tasks.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.Text-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.TokensThe basic units of text that a language model processes, typically representing words or word fragments.TransformerThe dominant neural network architecture for language models, using self-attention to process sequences.

Capabilities

Capabilities

Use Case Fit

Glossary