catai
mlc-llm
| catai | mlc-llm | |
|---|---|---|
| 7 | 89 | |
| 408 | 17,053 | |
| 1.7% | 3.7% | |
| 8.6 | 9.9 | |
| 3 months ago | 3 days ago | |
| TypeScript | Python | |
| MIT License | 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.
catai
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Are you sure you are focusing on the right things? (venting)
The easiest tool I found is CatAI: https://github.com/ido-pluto/catai You just type 3 npm commands and THATS IT! You have your own Chat Web UI on your computer without hundrets of settings
- How to use CatAI to apologize to your boss
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Wizard-Vicuna-13B-Uncensored
I am a noob. I saw your comment on github and another post here. I am confused about what has changed and what us users have to do. Do we have to update llama.cpp and redownload all the models(I am using something called catai instead of the webui, i think it also uses llama.cpp)? How do we know which versions of the models are compatible with which vesions of llama.cpp?
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Google removes the waitlist on Bard today and will be available in 180 more countries
https://github.com/ggerganov/llama.cpp https://github.com/oobabooga/text-generation-webui https://github.com/mlc-ai/mlc-llm https://github.com/cocktailpeanut/dalai https://github.com/ido-pluto/catai (this is super easy to install but it doesnt provide an api or have integration with langchain)
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GPT For All 13B (/GPT4All-13B-snoozy-GPTQ) is Completely Uncensored, a great model
Pretty simple using catai.
- How to run something like chatgpt, locally?
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How to install Wizard-Vicuna
You can check out the original GitHub project here
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?
alpaca-electron - The simplest way to run Alpaca (and other LLaMA-based local LLMs) on your own computer
llama.cpp - LLM inference in C/C++
HyunGPT - chatbot thing
ggml - Tensor library for machine learning
AutoGPTQ - An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
dalai - The simplest way to run LLaMA on your local machine
llama-cpp-python - Python bindings for llama.cpp
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.