flake
mlc-llm
| flake | mlc-llm | |
|---|---|---|
| 5 | 89 | |
| 593 | 17,053 | |
| 3.9% | 3.7% | |
| 4.4 | 9.9 | |
| 7 days ago | 4 days ago | |
| Nix | Python | |
| GNU Affero General Public License v3.0 | Apache License 2.0 |
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.
flake
- Running AI Models on NixOS
- Nixified.Ai Release 2
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Llama.cpp: Full CUDA GPU Acceleration
> Ideally, there's Nix (and poetry2nix) that could take care of everything, but only a few folks write Flakes for their projects.
Relevant to "AI, Python, setting up is hard ... nix", there's stuff like:
https://github.com/nixified-ai/flake
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Can you substitute conda with Nix for Data Science and ML/AI?
However, I would reach out to the Nixified.ai folks about it, because I can see that the invoke.ai build script mentions pytorch and several other hard-to-install packages (albeit not detectron).
- A Nix flake for many AI projects
mlc-llm
- FLaNK 04 March 2024
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Ai on a android phone?
This one uses gpu, it doesn't support Mistral yet: https://github.com/mlc-ai/mlc-llm
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MLC vs llama.cpp
I have tried running mistral 7B with MLC on my m1 metal. And it kept crushing (git issue with description). Memory inefficiency problems.
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[Project] Scaling LLama2 70B with Multi NVIDIA and AMD GPUs under 3k budget
Project: https://github.com/mlc-ai/mlc-llm
- Scaling LLama2-70B with Multi Nvidia/AMD GPU
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AMD May Get Across the CUDA Moat
For LLM inference, a shoutout to MLC LLM, which runs LLM models on basically any API that's widely available: https://github.com/mlc-ai/mlc-llm
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ROCm Is AMD's #1 Priority, Executive Says
One of your problems might be that gfx1032 is not supported by AMD's ROCm packages, which has a laughably short list of supported hardware: https://rocm.docs.amd.com/en/latest/release/gpu_os_support.h...
The normal workaround is to assign the closest architecture, eg gfx1030, so `HSA_OVERRIDE_GFX_VERSION=10.3.0` might help
Also, it looks like some of your tested projects are OpenCL? For me, I do something like: `yay -S rocm-hip-sdk rocm-ml-sdk rocm-opencl-sdk` to cover all the bases.
My recent interest has been LLMs and this is my general step by step for those (llama.cpp, exllama) for those interested: https://llm-tracker.info/books/howto-guides/page/amd-gpus
I didn't port the docs back in, but also here's a step-by-step w/ my adventures getting TVM/MLC working w/ an APU: https://github.com/mlc-ai/mlc-llm/issues/787
From my experience, ROCm is improving, but there's a good reason that Nvidia has 90% market share even at big price premiums.
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Show HN: Ollama for Linux – Run LLMs on Linux with GPU Acceleration
Maybe they're talking about https://github.com/mlc-ai/mlc-llm which is used for web-llm (https://github.com/mlc-ai/web-llm)? Seems to be using TVM.
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Show HN: Fine-tune your own Llama 2 to replace GPT-3.5/4
you already have TVM for the cross platform stuff
see https://tvm.apache.org/docs/how_to/deploy/android.html
or https://octoml.ai/blog/using-swift-and-apache-tvm-to-develop...
or https://github.com/mlc-ai/mlc-llm
- Ask HN: Are you training and running custom LLMs and how are you doing it?
What are some alternatives?
nonguix - Nonguix mirror – pull requests ignored, please use upstream for that
llama.cpp - LLM inference in C/C++
guix-nonfree - Unofficial collection of packages that are not going to be accepted in to guix
ggml - Tensor library for machine learning
lit-llama - Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
llama_cpp.rb - llama_cpp provides Ruby bindings for llama.cpp
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
serving - A flexible, high-performance serving system for machine learning models
llama-cpp-python - Python bindings for llama.cpp
guix-nonfree
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.