Hy3 is a modern large language model; delivering ready-to-run, low-bit GGUF builds lets practitioners run it on consumer and server GPUs without rebuilding from scratch. This repository/pack supplies both quantized GGUF weights and the mixed-precision recipes + imatrix calibration workflow required to preserve quality while dramatically reducing disk and memory footprint.
Key Capabilities
- Quantized GGUF weights for llama.cpp (hy_v3): ready-made GGUF files targeting mixed-precision configurations so you can load Hy3 in llama.cpp builds that include hy_v3 support. This removes the need to convert large HF checkpoints yourself.
- Mixed-precision recipes with imatrix calibration: per-tensor/layer recipes (Q4_K_M, IQ1_M variants, MTP-aware recipes) focus bits where they matter (attention, embeddings, sensitive FFN layers) so quantization preserves generation quality while minimizing size.
- Optional MTP self-speculative decoding support: builds and recipes include variants that keep the MTP head and enable draft-based self-speculation for faster/safer decoding when the GGUF includes MTP data.
- Hardware sizing and trade-offs documented: recommended GPU setups and approximate weight sizes are provided (examples: IQ1_M ~83 GiB; Q4_K_M ~166 GiB; MTP adds ~2 GiB plus draft KV cache), so you can plan memory/context budgets ahead.
Who it's for & trade-offs
Great fit if you need to run Hy3 locally or on private servers and want prebuilt, tested quantized GGUFs plus reproducible recipes to re-quantize with your own calibration set. It's valuable when you care about balancing disk/memory savings against generation quality (and want MTP-enabled speculative decoding).
Look elsewhere if you need a small consumer-device model (these builds still expect large GPU resources for best latency) or if you prefer a fully managed cloud inference service—this package focuses on local/server deployment and quantization workflows rather than turnkey hosted APIs.
Notes: the bundle emphasizes reproducible quantization (convert→imatrix→quantize) and documents recommended llama.cpp commits and runtime flags; operational/installation steps and full examples remain in the original model card and repository.
