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
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LFM2.5-2.6B is carefully designed for device and ultra-fast server-side deployments with unmatched decode speed, prefill latency, and memory footprint ships with day-one support across the inference ecosystem:
> llama.cpp, MLX, vLLM, SGLang, ONNX
> Hardware: AMD, Qualcomm, Apple, NVIDIA, Intel
Two steps to set up your own local agent:
1) serve LFM2.5-2.6B behind an OpenAI-compatible endpoint
2) point your agent harness at it
We trained it inside the real agent harnesses people use.
> Four stages of post-training: SFT, expert specialization, multi-domain on-policy distillation, and agentic RL
> The last stage of post-training is multi-turn agentic RL through Pi, Hermes Agent, and OpenClaw
> Each rollout runs in its own sandbox, optimized with GRPO against an outcome reward: an LLM-as-a-judge rubric, programmatic checks, and a hard safety gate. The model arrives having already seen their tools, their system prompts, and their interaction patterns.
It suits high-volume agentic work on edge devices, where speed, privacy, and local deployment matter. For coding-heavy workloads or more complex agentic tasks, a larger model may still be the better fit.
LFM2.5-2.6B and LFM2.5-2.6B-Base are available now on Hugging Face:
> Blog: https://t.co/Qr22eVQacI
> LFM2.5-2.6B: https://t.co/2eXeVf9fTI
> LFM2.5-2.6B-Base: https://t.co/4zOCXW1WBr
> Docs: https://t.co/SyQJEpJd6h
open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Why it matters
You can run a tool-calling AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → locally behind an OpenAI-compatible endpoint, keeping data on the device at near-zero marginal cost, with reported tool-use scores above models several times its size.
