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LFM2.5-2.6B
LFM2.5-2.6B is Liquid AI's 2.69B-parameter text model for local tool use, multi-step agent workflows, data extraction, RAG, and long-context tasks.
The downloadable checkpoint has a 131,072-token context window and official native, GGUF, ONNX, and MLX options for phones, laptops, edge devices, and server runtimes. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A compact model trained for agent workflows
Liquid AI post-trained this 2.6B model for instruction following, tool use, and multi-step tasks. It always uses a reasoning step before returning an answer.
Why it stands out
128K context in a compact model
The model combines a 128K context window with local formats. It gives people a middle option between Liquid AI's smaller edge models and the larger 8B-A1B.
Availability
Weights, local formats, and a browser demo
The main and base checkpoints are available alongside GGUF, ONNX, and MLX versions, runtime guides, fine-tuning notebooks, and a WebGPU research-agent demo.
Why it matters
What makes it useful
This downloadable model gives builders a local option for multi-step agent tasks that might otherwise run through a hosted API. Its size, tool-call format, and long context let builders test workflows on their own hardware before deciding whether a larger model is necessary.
What to know
Where it fits
Liquid AI positions it for tool-driven assistants, extraction, retrieval-augmented generation, document-heavy tasks, and other local workflows where a compact model matters. Liquid AI does not recommend it for agentic coding or knowledge-heavy tasks, so those jobs may need a different model or stronger verification.
Notable points
What stands out
Liquid AI's speed and benchmark numbers come from its own tests on phones, Ryzen systems, Apple Silicon, and H100 GPUs. Results will vary with quantization, context length, hardware, prompts, and tools, so test the setup you plan to use.
Before using
What to review
Choose the format and runtime that fit the device: native Transformers, GGUF with llama.cpp, ONNX, MLX, vLLM, SGLang, or LM Studio.
Check memory and speed with the intended quantization and context length; the company's reported results are tied to specific hardware and configurations.
Check its tool calls and answers before letting it act on anything important, especially across long multi-step workflows.
Read the current LFM Open License v1.0 before commercial use. Its commercial-use terms include an annual-revenue threshold.
Use another model or extra verification for coding and knowledge-heavy work, which the official model card does not recommend here.
Reader fit
Who may find it relevant
Builders testing local or edge agents with tool calling and long context.
Teams comparing compact models for extraction, RAG, and repeated background tasks without per-token API billing.
People who need downloadable weights for CPU, Apple Silicon, ONNX, or GPU-serving setups.
Less relevant for readers who want a ready-made consumer assistant with no setup or model evaluation.
Editorial note
Why LifeHubber lists it
LifeHubber lists LFM2.5-2.6B because it brings tool calling and multi-step workflows to a compact model that can run locally. It sits between Liquid AI’s smaller edge models and its larger LFM2.5 option, giving people a practical middle choice. Its coding and knowledge limits are worth checking before deciding whether it fits the job.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Compare another size in the LFM2.5 family.
Use these pages to compare a lighter edge model with a larger mixture-of-experts option.
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