one-click-installers
llama.cpp
one-click-installers | llama.cpp | |
---|---|---|
18 | 775 | |
470 | 57,463 | |
- | - | |
8.9 | 10.0 | |
8 months ago | 3 days ago | |
Python | C++ | |
GNU Affero General Public License v3.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
one-click-installers
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amd gpus on windows support?
AMD does not offer installation options for ROCm on Windows. I'm not familiar with the workarounds to make it work; if you find a solution, you can contribute it to https://github.com/oobabooga/one-click-installers/
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Oobabooga for Windows
Running start_windows.bat should take care of everything.
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Quant-Cude Error?
Had the same issue, turns out I was using an old 1 click installer / updater, you need to use https://github.com/oobabooga/one-click-installers and reinstall everything from scratch
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Cant find the "start: file.
Are you sure you're looking at the right folder? start_windows.bat is there. It's listed in the source code: https://github.com/oobabooga/one-click-installers
- Any UI that allows Windows + AMD GPU ?
- WizardLM-30B-Uncensored
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13b-4bit-128g - Trying to run compressed model without success. ( problem exist only with 13b models for some reason ) No error code has been displayed.
one-click-installers/INSTRUCTIONS.TXT
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GPT4All: A little helper to get started
https://github.com/oobabooga/one-click-installers/issues/56 they explain it over here.
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Visual Studio compile errors
I solved this by adding the Individual components 2019 Windows 10 SDK, C++ CMake tools for Windows, and MSVC v142 - VS 2019 C++ build tools. See https://github.com/oobabooga/one-click-installers/issues/56
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python setup.py bdist_wheel did not run successfully.
It appears one of the extensions isn't pre-compiled on install. I believe you have the same problem as listed here. https://github.com/oobabooga/one-click-installers/issues/56
llama.cpp
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Ask HN: Affordable hardware for running local large language models?
Yes, Metal seems to allow a maximum of 1/2 of the RAM for one process, and 3/4 of the RAM allocated to the GPU overall. There’s a kernel hack to fix it, but that comes with the usual system integrity caveats. https://github.com/ggerganov/llama.cpp/discussions/2182
- Xmake: A modern C/C++ build tool
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Better and Faster Large Language Models via Multi-Token Prediction
For anyone interested in exploring this, llama.cpp has an example implementation here:
https://github.com/ggerganov/llama.cpp/tree/master/examples/...
- Llama.cpp Bfloat16 Support
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Fine-tune your first large language model (LLM) with LoRA, llama.cpp, and KitOps in 5 easy steps
Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.
- GGML Flash Attention support merged into llama.cpp
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Phi-3 Weights Released
well https://github.com/ggerganov/llama.cpp/issues/6849
- Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
- Llama.cpp Working on Support for Llama3
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Embeddings are a good starting point for the AI curious app developer
Have just done this recently for local chat with pdf feature in https://recurse.chat. (It's a macOS app that has built-in llama.cpp server and local vector database)
Running an embedding server locally is pretty straightforward:
- Get llama.cpp release binary: https://github.com/ggerganov/llama.cpp/releases
What are some alternatives?
GPTQ-for-LLaMa - 4 bits quantization of LLaMa using GPTQ
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
gpt4all - gpt4all: run open-source LLMs anywhere
gradio - Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
KoboldAI
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
WizardVicunaLM - LLM that combines the principles of wizardLM and vicunaLM
ggml - Tensor library for machine learning
micromamba-releases - Micromamba executables mirrored from conda-forge as Github releases
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM