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Today, we release LFM2.5-350M. Agentic loops at 350M parameters. A 350M model trained for reliable data extraction and tool use, where models at this scale typically struggle. <500MB when quantized, built for environments where compute, memory, and latency are constrained. 🧵
Trained on 28T tokens with scaled RL, LFM2.5-350M is a step change from LFM2-350M: > instruction following: 18.20 → 40.69 > data extraction: 11.67 → 32.45 > tool use: 22.95 → 44.11 These are the capabilities that matter in production.
What matters isn’t parameter count, it’s what you can actually run: > runs across CPUs, GPUs, and mobile > fast, efficient, low-latency > consistent structured outputs > reliable function calling and agent workflows This enables a different deployment model: move high-frequency, structured workloads closer to the data: on-device, on-prem, or at the edge instead of routing everything through a large cloud model.
Example use cases: > document processing pipelines running locally > lightweight agent workflows under tight latency > systems operating offline or on-device
Day 0 support across the stack: > Hardware: @AMD, @Intel, @Qualcomm > On-device: @lmstudio , @Cactuscompute, @RunAnywhereAI , @zeticai_ , @trymirai > Customization: @distil_labs
LFM2.5-350M: built for real workloads. > Blog: <a target="_blank" href="http://www.liquid.ai/blog/lfm2-5-350m-no-size-left-behind" color="blue">liquid.ai/blog/lfm2-5-35…</a> > Weights: <a target="_blank" href="https://huggingface.co/LiquidAI/LFM2.5-350M" color="blue">huggingface.co/LiquidAI/LFM2.…</a> > Docs: <a target="_blank" href="http://docs.liquid.ai" color="blue">docs.liquid.ai</a> > Playground: <a target="_blank" href="http://playground.liquid.ai" color="blue">playground.liquid.ai</a>