Low-light　image　enhancement　plays　a　central　role　in　various　downstream　computer　vision　tasks.　Vision　Transformers　(ViTs)　have　recently　been　adapted　for　low-level　image　processing　and　have　achieved　a　promising　performance.　However,　ViTs　process　images　in　a　window-　or　patch-based　manner,　compromising　their　computational　efficiency　and　long-range　dependency.　Additionally,　existing　ViTs　process　RGB　images　instead　of　RAW　data　from　sensors,　which　is　sub-optimal　when　it　comes　to　utilizing　the　rich　information　from　RAW　data.　We　propose　a　fully　end-to-end　Conv-Transformer-based　model,　RawFormer,　to　directly　utilize　RAW　data　for　low-light　image　enhancement.　RawFormer　has　a　structure　similar　to　that　of　U-Net,　but　it　is　integrated　with　a　thoughtfully　designed　Conv-Transformer　Fusing　(CTF)　block.　The　CTF　block　combines　local　attention　and　transposed　self-attention　mechanisms　in　one　module　and　reduces　the　computational　overhead　by　adopting　a　transposed　self-attention　operation.　Experiments　demonstrate　that　RawFormer　outperforms　state-of-the-art　models　by　a　significant　margin　on　low-light　RAW　image　enhancement　tasks.