Liquid AI Releases LFM2.5-2.6B: An On-Device Agentic Model With 128K Context, Tool Calling, And Open Weights

Liquid AI released LFM2.5-2.6B, an on-device agentic model with 2.69B total parameters, a 131,072-token context window, and a 128,000-token vocabulary, pre-trained on approximately 34 trillion tokens. The model, available in open weights on Hugging Face under the lfm1.0 license, supports tool calling and multi-step tasks on phones, laptops, PCs, and robots, with inference staying local so data never leaves the device and marginal cost per run is near zero. Liquid AI reports tool-use and instruction-following scores competitive with models nearly four times its size, and the model decodes at 220 tokens/s on an M5 Max in under 2.5 GB.

Liquid AI released LFM2.5-2.6B https://www.liquid.ai/blog/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. The model has 2.69B total parameters, a 131,072-token context window, and a 128,000-token vocabulary. Pre-training used approximately 34 trillion tokens. Two checkpoints shipped: LFM2.5-2.6B-Base https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base for fine-tuning, and LFM2.5-2.6B https://huggingface.co/LiquidAI/LFM2.5-2.6B post-trained for agentic workloads. Because inference stays local, data never leaves the device and the marginal cost of each run is near zero. Liquid AI reports tool-use and instruction-following scores competitive with models nearly four times its size. Is it deployable The answer is Yes. Both checkpoints are public on Hugging Face under the lfm1.0 license. Weights ship in native, GGUF https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF , MLX https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX , and ONNX https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX formats, with day-one support in llama.cpp, vLLM, SGLang, and LM Studio. Which companies : Solo developers and startups can pilot on hardware they already own. The model decodes at 220 tokens/s on an M5 Max in under 2.5 GB. Mid-market teams can self-host on one GPU: a single NVIDIA H100 SXM5 https://docs.liquid.ai/lfm/inference/vllm serves roughly 1.3B tokens per day. Enterprises and OEMs can push the same weights to device fleets through GGUF and ONNX. Fine-tuning is available via LoRA with TRL https://docs.liquid.ai/lfm/fine-tuning/trl and Unsloth https://docs.liquid.ai/lfm/fine-tuning/unsloth . Which industries : Liquid AI targets automotive https://www.liquid.ai/automotive , consumer electronics https://www.liquid.ai/consumer-electronics , industrial robotics https://www.liquid.ai/industrial-robotics , healthcare https://www.liquid.ai/healthcare , financial services https://www.liquid.ai/finance , e-commerce https://www.liquid.ai/e-commerce , and defense https://www.liquid.ai/defense . Regulated and air-gapped settings benefit most, since no prompt reaches a third-party API. Applications : Liquid AI recommends agentic workloads, tool use, data extraction, RAG, and long-context workflows. Practical builds include on-device assistants, offline document triage over 128K inputs, form and invoice extraction, robotics command parsing, and background agents that run continuously without per-token cost. Liquid AI explicitly does not recommend the model for agentic coding or knowledge-heavy tasks. Architecture and training budget LFM2.5-2.6B has 2.69B total parameters across 30 layers. The stack is 22 double-gated short convolution blocks plus 8 grouped-query attention blocks. Vocabulary size is 128,000 and context length is 131,072 tokens. Pre-training used approximately 34 trillion tokens. Liquid AI doubled the vocabulary to 128K by extending the existing tokenizer in place https://www.liquid.ai/blog/tokenizer-expansion rather than retraining from scratch. A dedicated mid-training phase extends context to 128K. The model covers 16 languages and is text-only. Four-stage post-training The base checkpoint becomes an agent through four stages. - First, two consecutive supervised fine-tuning rounds, with an SFT mix roughly seven times the size used for LFM2.5-8B-A1B. - Second, teacher specialization: one expert per domain, trained with reinforcement learning with verifiable rewards. - Third, multi-domain on-policy distillation, where the student rolls out under its own policy and each prompt routes to its domain teacher. - Fourth, agentic reinforcement learning with GRPO inside real harnesses, including Hermes Agent https://hermes-agent.nousresearch.com/ and OpenClaw https://openclaw.ai/ . Benchmarks Liquid AI compared LFM2.5-2.6B against gemma-4-E2B-it 5.1B , gemma-4-E4B-it 8B , Qwen3.5-4B 4.7B and Qwen3.5-9B 9.7B . | Benchmark | LFM2.5-2.6B | gemma-4-E4B-it | Qwen3.5-9B | |---|---|---|---| | ToolSandbox | 77.83 | 65.00 | 76.44 | | Multi-IF | 80.07 | 77.35 | 62.55 | | IFStruct | 85.49 | 76.65 | 78.50 | | IFBench | 59.17 | 39.24 | 56.47 | | BFCLv4 | 56.88 | 46.39 | 60.13 | It leads every instruction-following benchmark reported and nearly every tool use benchmark, trailing Qwen3.5-9B only on BFCLv4. Coding is where larger models keep an edge: LiveCodeBenchv6 is 59.41 versus 69.86 for Qwen3.5-9B. Interactive explainer Key Takeaways - 2.69B params, 30 layers 22 short-conv + 8 GQA , 128K context, ~34T training tokens. - Beats gemma-4-E4B-it and Qwen3.5-9B on ToolSandbox, Multi-IF and IFStruct. - 220 tok/s on M5 Max, 30 tok/s on phone, under 2.5 GB memory. - Open weights under lfm1.0 , with GGUF, MLX and ONNX from day one. Check out the Technical details, LFM2.5-2.6B , and Also, feel free to follow us on LFM2.5-2.6B-Base https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base . and don’t forget to join our Twitter https://x.com/intent/follow?screen name=marktechpost and Subscribe to 150k+ML SubReddit https://www.reddit.com/r/machinelearningnews/ . Wait are you on telegram? our Newsletter https://www.aidevsignals.com/ now you can join us on telegram as well. https://t.me/machinelearningresearchnews Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us https://forms.gle/wbash1wF6efRj8G58 Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.