The hybrid quant employs different quantization levels on a per layer basis to increased
flexibility of trading off performance vs file size. Less parameter bits are used at deep layers
and more bits at cortex layers to simultaneously optimize quantized size and model performance.
This quant was optimized for similar size to Q4_K_M quant with improved performance while using all K quants
to increase processing efficiency on old GPUs or CPUs.
This quant was optimized for good reasoning performance across a curated set of test prompts.
Comparison:
Quant
size
PPL
Comment
Q4_K_M
8.2e9
6.1
-
Q4_K_H
8.3e9
6.1
slightly larger than Q4_K_M
Usage:
This is a vision capable model. It can be used together with its multimedia projector layers to process images and text inputs
and generate text outputs. The mmproj file is made available in this repository. To test vision mode follow the docs in the mtmd
readme in the tools directory of the source tree https://github.com/ggml-org/llama.cpp/blob/master/tools/mtmd/README.md .
Performance:
On a 4070 with cuda backend gen rate is about 50tps with ~15k context available (F16 KV) and ~28k available (Q8_0 KV)
The model is trained at 16k context which can be extended to 256k using YARN:
For other than 256k context set rope_scale to the fraction of configured context size / 16384.0.
NOTE: For improved inference performance, context size may be limited to 16k. Unless large context is needed, this is recommended for best performance.
