KoboldCPP-v1.55.yr0-ROCm
KoboldCPP-v1.55.yr0-ROCm
- Added Dynamic Temperature (DynaTemp), which is specified by a Temperature Value and a Temperature Range (Credits: @kalomaze). When used, the actual temperature is allowed to be automatically adjusted dynamically between DynaTemp ± DynaTempRange. For example, setting temperature=0.4 and dynatemp_range=0.1 will result in a minimum temp of 0.3 and max of 0.5. For ease of use, a UI to select min and max temperature for dynatemp directly is also provided in Lite. Both inputs will work and auto update the other.
- Try to reuse cloudflared file when running remote tunnel, but also handle if cloudflared fails to download correctly.
- Added a field to show the most recently used seed in the perf endpoint
- Switched cuda pool malloc back to the old implementation
- Updated Lite, added support for DynaTemp
- Merged new improvements and fixes from upstream llama.cpp
- Various minor fixes.
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
