Custom ATX GGUF quantizations of Swift-1.5-Qwen3.8-27B-Uncensored-MTP featuring an embedded Multi-Token Prediction (MTP) draft head in blk.64, specifically optimized for high-throughput inference in llamAmpere and standard llama.cpp. While compatible with upstream llama.cpp, this model family is engineered for llamAmpere, a specialized high-performance fork tuned for NVIDIA Ampere (SM86) and Ada Lovelace architectures (RTX 3090 / RTX 4090). - MMA Fused Q8 Kernels (GGMLQ8TURBO3MMAFUSED="1"): Eliminates memory bandwidth bottlenecks and achieves peak Tensor Core utilization. - Hardware-Accelerated MTP Decoding (--spec-type draft-mtp): Reaches 45-60+ tokens/s on a single RTX 3090 (24GB) via…
Independent publisher
Alejandro Melo
bjivanovich
Models in Library2
Datasets in Library0
Models on Hugging Face17
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Published tasks: Text generation.
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Models
Official GGUF quantized weights for swift-1.5-27b-coding, a specialized 27B parameter coding model fine-tuned using DoRA on bjivanovich/code-py-rust-cpp-50k (covering Python, Rust, C++, and multi-step reasoning). All quantizations were generated using an importance matrix (imatrix) calibrated directly on domain-specific programming samples.