sherpa
mlc-llm
| sherpa | mlc-llm | |
|---|---|---|
| 5 | 89 | |
| 248 | 17,053 | |
| 6.5% | 3.7% | |
| 5.8 | 9.9 | |
| 3 months ago | 6 days ago | |
| Dart | 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.
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sherpa
- ChatGPT for Android
- Sherpa(Llama.cpp for Android) New Pull request add latest pulls from llama.cpp and it's faster now with no more crash. (apk link in description)
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You can watermark GPT's outputs discreetly
I use Sherpa to run 7B and 13B LLaMA/Alpaca models on my phone: https://github.com/Bip-Rep/sherpa
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GPT-4 Week 4. The rise of Agents and the beginning of the Simulation era
If you are interested I made an app that runs on mobile phone that runs llama.cpp or vicuna. There is also a Mac and a windows version as it is built with flutter. https://github.com/Bip-Rep/sherpa
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We made a mobile app using llama.cpp
I just wanted to share that i was able to build an APK with recompiled llama as a shared c++ library. You can try it on my GitHub: https://github.com/Bip-Rep/sherpa
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?
GPT-4-Unlimited-Tools - Custom Plugins for GPT-4 (easy web app interface)
llama.cpp - LLM inference in C/C++
sherpa - A mobile Implementation of llama.cpp
ggml - Tensor library for machine learning
titanium-web-proxy - A cross-platform asynchronous HTTP(S) proxy server in C#.
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.
llama-cpp-python - Python bindings for llama.cpp
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
alpaca-electron - The simplest way to run Alpaca (and other LLaMA-based local LLMs) on your own computer
EasyLM - Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.