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koboldcpp/README.md
HanishKVC 2ac95c9d56
SimpleChat: Simple histogram/repeatMatching driven garbageTrimming, Settings UI, Streaming mode, OpenAi Compat (Model, Authorization Bearer), Save/Restore session, Auto Settings UI (#7548) 
* SimpleChat:DU:BringIn local helper js modules using importmap

Use it to bring in a simple trim garbage at end logic, which is
used to trim received response.

Also given that importmap assumes esm / standard js modules, so
also global variables arent implicitly available outside the
modules. So add it has a member of document for now

* SimpleChat:DU: Add trim garbage at end in loop helper

* SimpleChat:DU:TrimGarbage if unable try skip char and retry

* SimpleChat:DU: Try trim using histogram based info

TODO: May have to add max number of uniq chars in histogram at
end of learning phase.

* SimpleChat:DU: Switch trim garbage hist based to maxUniq simple

Instead of blindly building histogram for specified substring
length, and then checking if any new char within specified min
garbage length limit, NOW exit learn state when specified maxUniq
chars are found. Inturn there should be no new chars with in
the specified min garbage length required limit.

TODO: Need to track char classes like alphabets, numerals and
special/other chars.

* SimpleChat:DU: Bring in maxType to the mix along with maxUniq

Allow for more uniq chars, but then ensure that a given type of
char ie numerals or alphabets or other types dont cross the
specified maxType limit. This allows intermixed text garbage
to be identified and trimmed.

* SimpleChat:DU: Cleanup debug log messages

* SimpleChat:UI: Move html ui base helpers into its own module

* SimpleChat:DU:Avoid setting frequence/Presence penalty

Some models like llama3 found to try to be over intelligent by
repeating garbage still, but by tweaking the garbage a bit so that
it is not exactly same. So avoid setting these penalties and let
the model's default behaviour work out, as is.

Also the simple minded histogram based garbage trimming from end,
works to an extent, when the garbage is more predictable and
repeatative.

* SimpleChat:UI: Add and use a para-create-append helper

Also update the config params dump to indicate that now one needs
to use document to get hold of gMe global object, this is bcas of
moving to module type js.

Also add ui.mjs to importmap

* SimpleChat:UI: Helper to create bool button and use it wrt settings

* SimpleChat:UI: Add Select helper and use it wrt ChatHistoryInCtxt

* SimpleChat:UI:Select: dict-name-value, value wrt default, change

Take a dict/object of name-value pairs instead of just names.
Inturn specify the actual value wrt default, rather than the
string representing that value.

Trap the needed change event rather than click wrt select.

* SimpleChat:UI: Add Div wrapped label+element helpers

Move settings related elements to use the new div wrapped ones.

* SimpleChat:UI:Add settings button and bring in settings ui

* SimpleChat:UI:Settings make boolean button text show meaning

* SimpleChat: Update a bit wrt readme and notes in du

* SimpleChat: GarbageTrim enable/disable, show trimmed part ifany

* SimpleChat: highlight trim, garbage trimming bitmore aggressive

Make it easy for end user to identified the trimmed text.

Make garbage trimming logic, consider a longer repeat garbage
substring.

* SimpleChat: Cleanup a bit wrt Api end point related flow

Consolidate many of the Api end point related basic meta data into
ApiEP class.

Remove the hardcoded ApiEP/Mode settings from html+js, instead use
the generic select helper logic, inturn in the settings block.

Move helper to generate the appropriate request json string based
on ApiEP into SimpleChat class itself.

* SimpleChat:Move extracting assistant response to SimpleChat class

so also the trimming of garbage.

* SimpleChat:DU: Bring in both trim garbage logics to try trim

* SimpleChat: Cleanup readme a bit, add one more chathistory length

* SimpleChat:Stream:Initial handshake skeleton

Parse the got stream responses and try extract the data from it.

It allows for a part read to get a single data line or multiple
data line. Inturn extract the json body and inturn the delta
content/message in it.

* SimpleChat: Move handling oneshot mode server response

Move handling of the oneshot mode server response into SimpleChat.

Also add plumbing for moving multipart server response into same.

* SimpleChat: Move multi part server response handling in

* SimpleChat: Add MultiPart Response handling, common trimming

Add logic to call into multipart/stream server response handling.

Move trimming of garbage at the end into the common handle_response
helper.

Add new global flag to control between oneshot and multipart/stream
mode of fetching response. Allow same to be controlled by user.

If in multipart/stream mode, send the stream flag to the server.

* SimpleChat: show streamed generative text as it becomes available

Now that the extracting of streamed generated text is implemented,
add logic to show the same on the screen.

* SimpleChat:DU: Add NewLines helper class

To work with an array of new lines. Allow adding, appending,
shifting, ...

* SimpleChat:DU: Make NewLines shift more robust and flexible

* SimpleChat:HandleResponseMultiPart using NewLines helper

Make handle_response_multipart logic better and cleaner. Now it
allows for working with the situation, where the delta data line
got from server in stream mode, could be split up when recving,
but still the logic will handle it appropriately.

ALERT: Rather except (for now) for last data line wrt a request's
response.

* SimpleChat: Disable console debug by default by making it dummy

Parallely save a reference to the original func.

* SimpleChat:MultiPart/Stream flow cleanup

Dont try utf8-decode and newlines-add_append if no data to work on.

If there is no more data to get (ie done is set), then let NewLines
instance return line without newline at end, So that we dont miss
out on any last-data-line without newline kind of scenario.

Pass stream flag wrt utf-8 decode, so that if any multi-byte char
is only partly present in the passed buffer, it can be accounted
for along with subsequent buffer. At sametime, bcas of utf-8's
characteristics there shouldnt be any unaccounted bytes at end,
for valid block of utf8 data split across chunks, so not bothering
calling with stream set to false at end. LATER: Look at TextDecoder's
implementation, for any over intelligence, it may be doing..
If needed, one can use done flag to account wrt both cases.

* SimpleChat: Move baseUrl to Me and inturn gMe

This should allow easy updating of the base url at runtime by the
end user.

* SimpleChat:UI: Add input element helper

* SimpleChat: Add support for changing the base url

This ensures that if the user is running the server with a
different port or wants to try connect to server on a different
machine, then this can be used.

* SimpleChat: Move request headers into Me and gMe

Inturn allow Authorization to be sent, if not empty.

* SimpleChat: Rather need to use append to insert headers

* SimpleChat: Allow Authorization header to be set by end user

* SimpleChat:UI+: Return div and element wrt creatediv helpers

use it to set placeholder wrt Authorization header.

Also fix copy-paste oversight.

* SimpleChat: readme wrt authorization, maybe minimal openai testing

* SimpleChat: model request field for openai/equivalent compat

May help testing with openai/equivalent web services, if they
require this field.

* SimpleChat: readme stream-utf-8 trim-english deps, exception2error

* Readme: Add a entry for simplechat in the http server section

* SimpleChat:WIP:Collate internally, Stream mode Trap exceptions

This can help ensure that data fetched till that point, can be
made use of, rather than losing it.

On some platforms, the time taken wrt generating a long response,
may lead to the network connection being broken when it enters
some user-no-interaction related power saving mode.

* SimpleChat:theResp-origMsg: Undo a prev change to fix non trim

When the response handling was moved into SimpleChat, I had changed
a flow bit unnecessarily and carelessly, which resulted in the non
trim flow, missing out on retaining the ai assistant response.

This has been fixed now.

* SimpleChat: Save message internally in handle_response itself

This ensures that throwing the caught exception again for higher
up logic, doesnt lose the response collated till that time.

Go through theResp.assistant in catch block, just to keep simple
consistency wrt backtracing just in case.

Update the readme file.

* SimpleChat:Cleanup: Add spacing wrt shown req-options

* SimpleChat:UI: CreateDiv Divs map to GridX2 class

This allows the settings ui to be cleaner structured.

* SimpleChat: Show Non SettingsUI config field by default

* SimpleChat: Allow for multiline system prompt

Convert SystemPrompt into a textarea with 2 rows. Reduce
user-input-textarea to 2 rows from 3, so that overall
vertical space usage remains same.

Shorten usage messages a bit, cleanup to sync with settings ui.

* SimpleChat: Add basic skeleton for saving and loading chat

Inturn when ever a chat message (system/user/model) is added,
the chat will be saved into browser's localStorage.

* SimpleChat:ODS: Add a prefix to chatid wrt ondiskstorage key

* SimpleChat:ODS:WIP:TMP: Add UI to load previously saved chat

This is a temporary flow

* SimpleChat:ODS:Move restore/load saved chat btn setup to Me

This also allows being able to set the common system prompt
ui element to loaded chat's system prompt.

* SimpleChat:Readme updated wrt save and restore chat session info

* SimpleChat:Show chat session restore button, only if saved session

* SimpleChat: AutoCreate ChatRequestOptions settings to an extent

* SimpleChat: Update main README wrt usage with server
2024-06-02 02:20:18 +10:00

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koboldcpp

KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models. It's a single self contained distributable from Concedo, that builds off llama.cpp, and adds a versatile Kobold API endpoint, additional format support, Stable Diffusion image generation, backward compatibility, as well as a fancy UI with persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and everything Kobold and Kobold Lite have to offer.

Preview Preview Preview Preview

Windows Usage

  • Download the latest .exe release here or clone the git repo.
  • Windows binaries are provided in the form of koboldcpp.exe, which is a pyinstaller wrapper for a few .dll files and koboldcpp.py. You can also rebuild it yourself with the provided makefiles and scripts.
  • Weights are not included, you can use the official llama.cpp quantize.exe to generate them from your official weight files (or download them from other places such as TheBloke's Huggingface.
  • To run, simply execute koboldcpp.exe.
  • Launching with no command line arguments displays a GUI containing a subset of configurable settings. Generally you dont have to change much besides the Presets and GPU Layers. Read the --help for more info about each settings.
  • By default, you can connect to http://localhost:5001
  • You can also run it using the command line. For info, please check koboldcpp.exe --help

Improving Performance

  • (Nvidia Only) GPU Acceleration: If you're on Windows with an Nvidia GPU you can get CUDA support out of the box using the --usecublas flag, make sure you select the correct .exe with CUDA support.
  • Any GPU Acceleration: As a slightly slower alternative, try CLBlast with --useclblast flags for a slightly slower but more GPU compatible speedup.
  • GPU Layer Offloading: Want even more speedup? Combine one of the above GPU flags with --gpulayers to offload entire layers to the GPU! Much faster, but uses more VRAM. Experiment to determine number of layers to offload, and reduce by a few if you run out of memory.
  • Increasing Context Size: Try --contextsize 4096 to 2x your context size! without much perplexity gain. Note that you'll have to increase the max context in the Kobold Lite UI as well (click and edit the number text field).
  • If you are having crashes or issues, you can try turning off BLAS with the --noblas flag. You can also try running in a non-avx2 compatibility mode with --noavx2. Lastly, you can try turning off mmap with --nommap.

For more information, be sure to run the program with the --help flag, or check the wiki.

Run on Colab

  • KoboldCpp now has an official Colab GPU Notebook! This is an easy way to get started without installing anything in a minute or two. Try it here!.
  • Note that KoboldCpp is not responsible for your usage of this Colab Notebook, you should ensure that your own usage complies with Google Colab's terms of use.

OSX and Linux

Linux Usage (Precompiled Binary, Recommended)

On Linux, we provide a koboldcpp-linux-x64-cuda1150 PyInstaller prebuilt binary on the releases page for modern systems. Simply download and run the binary.

Alternatively, you can also install koboldcpp to the current directory by running the following terminal command:

curl -fLo koboldcpp https://github.com/LostRuins/koboldcpp/releases/latest/download/koboldcpp-linux-x64-cuda1150 && chmod +x koboldcpp

After running this command you can launch Koboldcpp from the current directory using ./koboldcpp in the terminal (for CLI usage, run with --help).

Linux Usage (koboldcpp.sh automated compiler script)

when you can't use the precompiled binary directly, we provide an automated build script which uses conda to obtain all dependencies, and generates (from source) a ready-to-use a pyinstaller binary for linux users. Simply execute the build script with ./koboldcpp.sh dist and run the generated binary. (Not recomended for systems that already have an existing installation of conda. Dependencies: curl, bzip2)

./koboldcpp.sh # This launches the GUI for easy configuration and launching (X11 required).
./koboldcpp.sh --help # List all available terminal commands for using Koboldcpp, you can use koboldcpp.sh the same way as our python script and binaries.
./koboldcpp.sh rebuild # Automatically generates a new conda runtime and compiles a fresh copy of the libraries. Do this after updating Koboldcpp to keep everything functional.
./koboldcpp.sh dist # Generate your own precompiled binary (Due to the nature of Linux compiling these will only work on distributions equal or newer than your own.)

OSX and Linux Manual Compiling

  • Otherwise, you will have to compile your binaries from source. A makefile is provided, simply run make.

  • If you want you can also link your own install of OpenBLAS manually with make LLAMA_OPENBLAS=1

  • If you want you can also link your own install of CLBlast manually with make LLAMA_CLBLAST=1, for this you will need to obtain and link OpenCL and CLBlast libraries.

    • For Arch Linux: Install cblas openblas and clblast.
    • For Debian: Install libclblast-dev and libopenblas-dev.

  • You can attempt a CuBLAS build with LLAMA_CUBLAS=1. You will need CUDA Toolkit installed. Some have also reported success with the CMake file, though that is more for windows.

  • For a full featured build (all backends), do make LLAMA_OPENBLAS=1 LLAMA_CLBLAST=1 LLAMA_CUBLAS=1 LLAMA_VULKAN=1. (Note that LLAMA_CUBLAS=1 will not work on windows, you need visual studio)

  • After all binaries are built, you can run the python script with the command koboldcpp.py [ggml_model.bin] [port]

  • Note: Many OSX users have found that the using Accelerate is actually faster than OpenBLAS. To try, you may wish to run with --noblas and compare speeds.

Arch Linux Packages

There are some community made AUR packages available: CUBLAS, and HIPBLAS. They are intended for users with NVIDIA GPUs, and users with a supported AMD GPU. Note that these packages may be outdated, and it's probably better to use official KoboldCpp binaries.

Compiling on Windows

  • You're encouraged to use the .exe released, but if you want to compile your binaries from source at Windows, the easiest way is:
    • Use the latest release of w64devkit (https://github.com/skeeto/w64devkit). Be sure to use the "vanilla one", not i686 or other different stuff. If you try they will conflit with the precompiled libs!
    • Make sure you are using the w64devkit integrated terminal, then run 'make' at the KoboldCpp source folder. This will create the .dll files.
    • If you want to generate the .exe file, make sure you have the python module PyInstaller installed with pip ('pip install PyInstaller').
    • Run the script make_pyinstaller.bat at a regular terminal (or Windows Explorer).
    • The koboldcpp.exe file will be at your dist folder.
  • If you wish to use your own version of the additional Windows libraries (OpenCL, CLBlast and OpenBLAS), you can do it with:
    • OpenCL - tested with https://github.com/KhronosGroup/OpenCL-SDK . If you wish to compile it, follow the repository instructions. You will need vcpkg.
    • CLBlast - tested with https://github.com/CNugteren/CLBlast . If you wish to compile it you will need to reference the OpenCL files. It will only generate the ".lib" file if you compile using MSVC.
    • OpenBLAS - tested with https://github.com/xianyi/OpenBLAS .
    • Move the respectives .lib files to the /lib folder of your project, overwriting the older files.
    • Also, replace the existing versions of the corresponding .dll files located in the project directory root (e.g. libopenblas.dll).
    • You can attempt a CuBLAS build with using the provided CMake file with visual studio. If you use the CMake file to build, copy the koboldcpp_cublas.dll generated into the same directory as the koboldcpp.py file. If you are bundling executables, you may need to include CUDA dynamic libraries (such as cublasLt64_11.dll and cublas64_11.dll) in order for the executable to work correctly on a different PC.
    • Make the KoboldCPP project using the instructions above.

Compiling on Android (Termux Installation)

  • Install and run Termux from F-Droid
  • Enter the command termux-change-repo and choose Mirror by BFSU
  • Install dependencies with pkg install wget git python (plus any other missing packages)
  • Install dependencies apt install openssl (if needed)
  • Clone the repo git clone https://github.com/LostRuins/koboldcpp.git
  • Navigate to the koboldcpp folder cd koboldcpp
  • Build the project make
  • Grab a small GGUF model, such as wget https://huggingface.co/concedo/KobbleTinyV2-1.1B-GGUF/resolve/main/KobbleTiny-Q4_K.gguf
  • Start the python server python koboldcpp.py --model KobbleTiny-Q4_K.gguf
  • Connect to http://localhost:5001 on your mobile browser
  • If you encounter any errors, make sure your packages are up-to-date with pkg up
  • GPU acceleration for Termux may be possible but I have not explored it. If you find a good cross-device solution, do share or PR it.

AMD

Docker

Questions and Help

  • First, please check out The KoboldCpp FAQ and Knowledgebase which may already have answers to your questions! Also please search through past issues and discussions.
  • If you cannot find an answer, open an issue on this github, or find us on the KoboldAI Discord.

Considerations

  • For Windows: No installation, single file executable, (It Just Works)
  • Since v1.0.6, requires libopenblas, the prebuilt windows binaries are included in this repo. If not found, it will fall back to a mode without BLAS.
  • Since v1.15, requires CLBlast if enabled, the prebuilt windows binaries are included in this repo. If not found, it will fall back to a mode without CLBlast.
  • Since v1.33, you can set the context size to be above what the model supports officially. It does increases perplexity but should still work well below 4096 even on untuned models. (For GPT-NeoX, GPT-J, and LLAMA models) Customize this with --ropeconfig.
  • Since v1.42, supports GGUF models for LLAMA and Falcon
  • Since v1.55, lcuda paths on Linux are hardcoded and may require manual changes to the makefile if you do not use koboldcpp.sh for the compilation.
  • Since v1.60, provides native image generation with StableDiffusion.cpp, you can load any SD1.5 or SDXL .safetensors model and it will provide an A1111 compatible API to use.
  • I plan to keep backwards compatibility with ALL past llama.cpp AND alpaca.cpp models. But you are also encouraged to reconvert/update your models if possible for best results.

License

  • The original GGML library and llama.cpp by ggerganov are licensed under the MIT License
  • However, Kobold Lite is licensed under the AGPL v3.0 License
  • The other files are also under the AGPL v3.0 License unless otherwise stated

Notes

  • Generation delay scales linearly with original prompt length. If OpenBLAS is enabled then prompt ingestion becomes about 2-3x faster. This is automatic on windows, but will require linking on OSX and Linux. CLBlast speeds this up even further, and --gpulayers + --useclblast or --usecublas more so.
  • I have heard of someone claiming a false AV positive report. The exe is a simple pyinstaller bundle that includes the necessary python scripts and dlls to run. If this still concerns you, you might wish to rebuild everything from source code using the makefile, and you can rebuild the exe yourself with pyinstaller by using make_pyinstaller.bat
  • API documentation available at /api and https://lite.koboldai.net/koboldcpp_api
  • Supported GGML models (Includes backward compatibility for older versions/legacy GGML models, though some newer features might be unavailable):
    • All up-to-date GGUF models are supported (Mistral/Mixtral/QWEN/Gemma and more)
    • LLAMA and LLAMA2 (LLaMA / Alpaca / GPT4All / Vicuna / Koala / Pygmalion 7B / Metharme 7B / WizardLM and many more)
    • GPT-2 / Cerebras
    • GPT-J
    • RWKV
    • GPT-NeoX / Pythia / StableLM / Dolly / RedPajama
    • MPT models
    • Falcon (GGUF only)
    • Stable Diffusion and SDXL models