Try LFM • Docs • LEAP • Discord LFM2.5 230M Base LFM2.5 is a new family of hybrid models designed for on device deployment . It builds on the LFM2 architecture with extended pre training and reinforcement learning. Find more information about LFM2.5 230M in our blog post. 🗒️ Model Details Model Parameters Description LFM2.5 230M Base 230M Pre trained base model for fine tuning LFM2.5 230M 230M General purpose instruction tuned model LFM2.5 230M is a general purpose text only model with the following features: Number of parameters : 230M Number of layers : 14 (8 double gated LIV convolution blocks + 6 GQA blocks) Training budget : 28T tokens Context length : 32,768 tokens Vocabulary size : 65,536 Knowledge cutoff : Mid 2024 Languages : English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Spanish This pre trained checkpoint is only recommended for tasks that require heavy fine tuning, like language specific (e.g., Japanese) or domain specific (e.g., medical) assistants, training on proprietary data, or experimenting with novel post training approaches. 🏃 Inference LFM2.5 is supported by many inference frameworks. See the Inference documentation for the full list.…
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