LFM2.5-VL-3B
A vision-language foundation model in Liquid AI's LFM2.5 series, whose text backbone was weight-averaged with LFM2.5-2.6B.
Timeline (last 90 days)
LFM2.5-24B-A2B is a pure-text reasoning model with no vision tower, always begins with a <think> block, and those needing image capabilities should use LFM2.5-VL-3B (the highest model in the LFM2.5 VL series).
The d1-3B model has about 3.1 billion parameters, is trained on the LFM2.5-VL-3B vision-language backbone, and supports text and image input.
d1-3B has 3.12B parameters, its vision encoder is a shape-optimized version of SigLIP2 NaFlex (400M), its context length is 32,768 tokens, its vocabulary is 128,000, its weight format is Safetensors/BF16, its base model is LFM2.5-VL-3B (the text backbone was weight-averaged with LFM2.5-2.6B), and its license is lfm1.0.
d1-3B averages 74.1 across 11 image benchmarks, versus 73.9 for LFM2.5-VL-3B; on VL-RewardBench d1-3B scores 65.0, far ahead of LFM2.5-VL-3B's 50.9, but it lags on CV-Bench (82.1 vs 87.6), VisualWebBench (71.4 vs 78.3), and POPE (88.5 vs 90.1).
All Facts (4)
LFM2.5-24B-A2B is a pure-text reasoning model with no vision tower, always begins with a <think> block, and those needing image capabilities should use LFM2.5-VL-3B (the highest model in the LFM2.5 VL series).
50%UnverifiedThe d1-3B model has about 3.1 billion parameters, is trained on the LFM2.5-VL-3B vision-language backbone, and supports text and image input.
50%Unverifiedd1-3B has 3.12B parameters, its vision encoder is a shape-optimized version of SigLIP2 NaFlex (400M), its context length is 32,768 tokens, its vocabulary is 128,000, its weight format is Safetensors/BF16, its base model is LFM2.5-VL-3B (the text backbone was weight-averaged with LFM2.5-2.6B), and its license is lfm1.0.
50%Unverifiedd1-3B averages 74.1 across 11 image benchmarks, versus 73.9 for LFM2.5-VL-3B; on VL-RewardBench d1-3B scores 65.0, far ahead of LFM2.5-VL-3B's 50.9, but it lags on CV-Bench (82.1 vs 87.6), VisualWebBench (71.4 vs 78.3), and POPE (88.5 vs 90.1).
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