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@github-actions github-actions released this 28 Jan 05:14
· 1656 commits to main since this release
v1.56.yr0-ROCm
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KoboldCPP-v1.56.yr0-ROCm

Windows build does not contain the Vulkan backend yet.

  • NEW: Added early support for new Vulkan GPU backend by @0cc4m. You can try it out with the command --usevulkan (gpu id) or via the GUI launcher. Now included with the Windows and Linux prebuilt binaries.
  • Updated and merged the new GGML backend rework from upstream. This update includes many extensive fixes, improvements and changes across over a hundred commits. Support for earlier non-gguf models has been preserved via a fossilized earlier version of the library. Please open an issue if you encounter problems. The Wiki and Readme have been updated too.
  • Added support for setting dynatemp_exponent, previously was defaulted at 1.0. Support added over API and in Lite.
  • Fixed issues with Linux CUDA on Pascal, added more flags to handle conda and colab builds correctly.
  • Added support for Old CPU fallbacks (NoAVX2 and Failsafe modes) in build targets in the Linux prebuilt binary (and koboldcpp.sh)
  • Added missing 48k context option, fixed clearing file selection, better abort handling support, fixed aarch64 termux builds, various other fixes.
  • Updated Kobold Lite with many improvements and new features:
    • NEW: Added XTTS API Server support (Local AI powered text-to-speech).
    • Added option to let AI impersonate you for a turn in a chat.
    • HD image generation options.
    • Added popup-on-complete browser notification options.
    • Improved DynaTemp wizard, added options to set exponent
    • Bugfixes, padding adjustments, A1111 parameter fixes, image color fixes for invert color mode.

To use on Windows, download and run the koboldcpp_rocm.exe, which is a one-file pyinstaller OR download koboldcpp_rocm_files.zip and run python koboldcpp.py (additional python pip modules might need installed, like customtkinter and tk or python-tk.
To use on Linux, clone the repo and build with make LLAMA_HIPBLAS=1 -j4 (-j4 can be adjusted to your number of CPU threads for faster build times)

For a full Linux build, make sure you have the OpenBLAS and CLBlast packages installed:
For Arch Linux: Install cblas openblas and clblast.
For Debian: Install libclblast-dev and libopenblas-dev.
then run make LLAMA_HIPBLAS=1 LLAMA_OPENBLAS=1 LLAMA_CLBLAST=1 -j4

If you're using NVIDIA, you can try koboldcpp.exe at LostRuin's upstream repo here
If you don't need CUDA, you can use koboldcpp_nocuda.exe which is much smaller, also at LostRuin's repo.
To use on Linux, clone the repo and build with make LLAMA_HIPBLAS=1 -j4

Contributors

0cc4m
Assets 4
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