LFM2.5 350M Base

Name: LFM2.5 350M Base
Author: Liquid AI

by Liquid AI

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

Released November 2025128K context≈ 96,000 words

A compact, open-weight base model from LiquidAI built on their Liquid Foundation Model architecture. At 350M parameters, it operates at the smaller end of the spectrum, making it fast and resource-efficient, though its raw capacity is naturally limited compared to larger siblings. It handles long contexts up to 128K tokens, which is notable for its size.

Capabilities

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

Tool Use

Moderate

Instruction Following

LFM2.5 350M Base

by Liquid AI

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

Released November 2025128K context≈ 96,000 words

A compact, open-weight base model from LiquidAI built on their Liquid Foundation Model architecture. At 350M parameters, it operates at the smaller end of the spectrum, making it fast and resource-efficient, though its raw capacity is naturally limited compared to larger siblings. It handles long contexts up to 128K tokens, which is notable for its size.

Capabilities

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

Tool Use

Moderate

Instruction Following

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Base ModelA pretrained model that completes text patterns but hasn't been trained to follow instructions, serving as a starting point for customization through fine-tuning.Foundation ModelA large pre-trained model that serves as a starting point for building other models, rather than being trained from scratch.Foundation Model ArchitectureThe underlying structural design of a neural network that determines how it processes and learns from data, distinct from standard transformer designs.Liquid Foundation ModelAn alternative neural network architecture that uses continuous, adaptive transformations instead of fixed layers, allowing efficient processing with fewer parameters.Model ArchitectureThe underlying structural design of a neural network that determines how data flows through it and how it processes information.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.TokensThe basic units of text that a language model processes, typically representing words or word fragments.

LFM2.5 350M Base

Capabilities

LFM2.5 350M Base

Capabilities

Use Case Fit

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