LFM2.5-2.6B (2.6B parameters) was announced/posted 2026-08-04 as an on-device…

Brief

LFM2.5-2.6B is a 2.6B-parameter, on-device agentic model released by Liquid AI (posted 2026-08-04) claiming planner/tool-calling ability, 128K context and vocab, ~34T-token pretraining, and an open-weight LFM2 hybrid architecture. Benchmarks show it outperforming several much larger models, and the author provided a Desktop-cleaning demo plus setup docs (llama.cpp/LMStudio; OpenClaw/Hermes/Pi).

Why it matters

LFM2.5-2.6B (2.6B parameters) was announced/posted 2026-08-04 as an on-device agentic model that plans, calls tools, and executes multi-step tasks on phones, laptops, PCs and robots; Liquid AI says it was pre-trained on ~34T tokens, uses a flagship hybrid LFM2 architecture, has 128K context and 128K vocab, and is open-weight.

Key details

Benchmarks claim LFM2.5-2.6B matches or outperforms much larger models: ToolSandbox 77.83 (vs Qwen3.5-9B 76.44), Multi-IF 80.07 (vs Gemma-4-E4B-it 77.35), IFStruct 85.49 (vs Qwen3.5-9B 78.50); Liquid AI highlights low marginal cost and per-device privacy (data never leaves the device).

Practical demo and deployment: @helloiamleonie posted a demo of a 100% local agent cleaning her Desktop and published a step-by-step setup guide (docs.liquid.ai/examples/agen…) with recommended runtimes/servers (llama.cpp, LMStudio) and agent harnesses (OpenClaw, Hermes Agent, Pi); a video accompanies the release.

LFM2.5-2.6B is live!

At only 2.6B, it shouldn't be this good at agentic tasks.

Here you can see how a 100% local agent is cleaning up my Desktop.

I wrote a step-by-step guide on how to set it up yourself:
• Model: LFM2.5-2.6B
• Server of choice: llama.cpp, LMStudio, and more
• Harness of choice: OpenClaw, Hermes Agent, Pi

docs.liquid.ai/examples/agen…

Video

Liquid AI (@liquidai)

Today we release LFM2.5-2.6B, an agentic model that runs entirely on-device. It plans, calls tools, and works through multi-step tasks on phones, laptops, PCs, and robots. Data never leaves the device, and the marginal cost of each run is essentially zero.

> Pre-trained on ~34T tokens
> LFM2.5 flagship hybrid architecture
> Context length: 128K
> Vocab size: 128K
> balanced intelligence per watt
> customizable on a single GPU for any specialized task
> LFM2 open-weight license

Comparable or better scores compared to models up to nearly 4x its size:
> ToolSandbox 77.83, ahead of Qwen3.5-9B at 76.44
> Multi-IF 80.07, ahead of Gemma-4-E4B-it at 77.35
> IFStruct 85.49, ahead of Qwen3.5-9B at 78.50

🧵

— https://nitter.net/liquidai/status/2084640701669613906#m