Deploy local agents everywhere with LFM2.5-2.6B
热度趋势
百分比基于当前可用热度信号，而非评论数或独立用户人数。
官方发布带来Hugging Face 模型更新信号，适合跟踪能力变化、生态影响和后续落地。
LFM2.5-2.6B 旨在完全在设备上运行强大的代理，支持工具调用和多步工作流程。它足够小巧和快速，适用于从笔记本电脑到手机的日常硬件。这使得开发人员能够随处部署代理，确保设备上的数据隐私，并在没有云推理成本的情况下扩展使用。该模型在 AIME25、LiveCodeBenchv6 和 ToolSandbox 等多个基准测试中表现出色。
LFM2.5-2.6B is built to power capable agents entirely on-device. It supports tool calling and multi-step workflows while staying small and fast enough for everyday hardware, from laptops to phones. This enables developers to deploy agents everywhere, keep data private on the device, and scale usage without a cloud inference bill.
- Best-in-class agent: Competitive with models 4x larger on tool use, instruction following, and multi-step agentic tasks.
- Agentic reinforcement learning: Trained inside the most popular agentic harnesses to improve compatibility.
- Efficient inference: 220 tok/s on an Apple M5 Max and 113 tok/s on an AMD Ryzen CPU, in under 2.5 GB of memory.
How we built a reliable agentic model for edge devices
LFM2.5-2.6B is pre-trained on ~34T tokens, with a mid-training phase that extends the context window to 128K. Post-training then turns the base model into an agent in four stages:
- Supervised fine-tuning (SFT): two rounds of SFT, weighted heavily toward agentic data like tool use, web search, and harness trajectories.
- Teacher specialization: train one specialist teacher per domain (math, code, tool use, and more).
- Multi-domain on-policy distillation (MOPD): distill the specialist teachers into a single student.
- Agentic Reinforcement Learning (Agentic RL): run multi-turn RL inside real agent harnesses, where the model learns to work across different tools, system prompts, and multi-turn task environments.
The Agentic RL pipeline separates model optimization, inference, and environment execution into distinct components. The Training Engine optimizes the model, while the Rollout Engine generates actions using the latest policy. The RL framework orchestrates the training loop by launching rollouts, collecting trajectories and rewards, and updating the model.
Actions are executed within a Sandbox Service, where the Blackbox Harness hosts the agent (e.g., OpenClaw or Hermes Agent) and coordinates interactions with the task environment. The Harness Proxy lets us treat agentic harnesses as black boxes with no modification, while transparently capturing the token-level trajectories needed to reconstruct and validate RL training samples.
Benchmark results
We evaluated LFM2.5-2.6B against models up to ~4x its size on STEM, instruction following, tool use, and agentic tasks. It is the smallest model in the group, yet it competes with and often beats the rest.
Benchmark LFM2.5-2.6B (2.6B) gemma-4-E2B-it (5.1B) gemma-4-E4B-it (8B) Qwen3.5-4B (4.7B) Qwen3.5-9B (9.7B)
AA Omniscience -29.50 -74.47 -49.03 -54.30 -50.43
AIME25 51.87 26.33 34.27 49.33 56.07
LiveCodeBenchv6 59.41 54.92 63.77 60.85 69.86
IFBench 59.17 34.08 39.24 48.40 56.47
Multi-IF 80.07 69.44 77.35 55.67 62.55
IFStruct 85.49 64.85 76.65 36.25 78.50
BFCLv4 56.88 36.98 46.39 50.56 60.13
ToolSandbox 77.83 52.40 65.00 75.55 76.44
τ³-Bench Banking 5.67 3.35 4.12 5.45 5.15