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Cruz
@ViC305
God-fearing husband & father. AI Engineer. Fine-tuning + day-zero EXL3 & GGUF quants + benchmarks โ€ข Local LLMs โ€ข honest tok/s โ€ข fitting big models on small GPUs
ๅ‚ๅŠ  June 2009
976 ใƒ•ใ‚ฉใƒญใƒผไธญ    2.9K ใƒ•ใ‚กใƒณ
RED-SNOW-5.3-FLASH EXL3 is LIVE for both ONE and TWO DGX Sparks. ๐Ÿฅตโ„๏ธ ONE Spark: 2.49 bpw, 90.32% top-1, up to 61.7 tok/s TWO Sparks: 4.91 bpw, 96.85% top-1, KL 0.01174 New collab with @Blackfrost_AI. RED-SNOW is an abliteration of GLM-5.3-Flash with a cyber/security fine-tune, built for authorized red-team operations. I took the BF16 release and built two SAGE MixedK EXL3 targets depending on how much hardware you have. ๐—ข๐—ก๐—˜ ๐——๐—š๐—ซ ๐—ฆ๐—ฃ๐—”๐—ฅ๐—ž 2.49 bpw Top-1 agreement vs BF16: 90.32% Mean KLD: 0.1190 Single-Spark decode: ExLlamaV3: 38.7 tok/s TensorFold: 41.0 tok/s REXL3, co-collab stealth engine with @mr_r0b0t : 61.7 tok/s ๐Ÿ‘€๐Ÿคซ That is the pack for people trying to squeeze the entire model onto one Spark. ๐—ง๐—ช๐—ข ๐——๐—š๐—ซ ๐—ฆ๐—ฃ๐—”๐—ฅ๐—ž๐—ฆ 4.91 bpw Top-1 agreement: 96.85% Mean KLD: 0.01174 That is 9,917 / 10,240 positions choosing the same next token as the BF16 reference runtime. This is the quality-first build. SAGE does not just flatten every expert to the same precision. It allocates bits across the model based on sensitivity, which is exactly why I wanted separate 1-Spark and 2-Spark releases instead of forcing one compromise pack on everyone. Both are standalone EXL3 checkpoints. Vision components are packaged. MTP is preserved. Authorized research/security use only. Huge thanks to @Blackfrost_AI for trusting me with another collaboration. And yes, that 61.7 tok/s REXL3 result is something Iโ€™ll talk a lot more about soon. ๐Ÿฅถ Each repo has a proper deploy.md file for proper deployment to your Spark setup! Actively working on the 2 spark deploy.md so its validation stage but it will be updated shortly after validation! The one spark deploy.md is already validated! ONE Spark: TWO Sparks: RED-SNOW-5.3-FLASH COLLECTION:
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