fine-tuned LiquidAI’s LFM2.5-230M on Fable-5 coding traces - its better than I expected it to be
Summary
Fine-tuned LiquidAI's LFM2.5-230M model on Fable-5 coding traces, finding the results better than expected.
Similar Articles
LiquidAI/LFM2.5-230M
Liquid AI released LFM2.5-230M, a compact 230M-parameter hybrid model optimized for on-device deployment with fast edge inference speeds (213 tok/s on Galaxy S25 Ultra) and built for agentic tasks via reinforcement learning.
@liquidai: Introducing LFM2.5-230M: our smallest model yet, built to run fast anywhere (CPUs, NPUs, and GPUs) to enable agentic ta…
Liquid AI releases LFM2.5-230M, a small 230M parameter model optimized for fast inference on CPUs, NPUs, and GPUs, targeting agentic tasks on devices like phones and robots.
LiquidAI/LFM2.5-2.6B
Liquid AI released LFM2.5-2.6B, a 2.6B-parameter hybrid model optimized for on-device deployment with 128K context, agentic post-training, and fast inference (220 tok/s on Apple M5 Max) under 2.5GB memory.
Liquid AI releases LFM2.5-8B-A1B
Liquid AI released LFM2.5-8B-A1B, an edge model with a 128K context window, 38T tokens of pre-training, and large-scale reinforcement learning, capable of tool calling and complex tasks while fitting on an entry-level laptop.
LiquidAI LFM2.5-VL-3B: a 3.1B local VLM that beats Gemma-4 E4B — screen understanding 2.5 → 82.2
LiquidAI released LFM2.5-VL-3B, a 3.1B local vision-language model that outperforms Gemma-4 E4B and demonstrates a significant jump in screen understanding, making on-device AI more practical.