LLaMA_MPS
llama-mps
| LLaMA_MPS | llama-mps | |
|---|---|---|
| 4 | 4 | |
| 566 | 83 | |
| - | - | |
| 10.0 | 3.8 | |
| about 1 year ago | 8 months ago | |
| Python | Python | |
| GPL-3.0 | GNU General Public License v3.0 only |
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LLaMA_MPS
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A brief history of LLaMA models
Most places that recommend llama.cpp for mac fail to mention https://github.com/jankais3r/LLaMA_MPS, which runs unquantized 7b and 13b models on the M1/M2 GPU directly. It's slightly slower, (not a lot), and significantly lower energy usage. To me the win not having to quantize is huge; I wish more people knew about it.
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Databricks Releases 15K Record Training Corpus for Instruction Tuning LLMs
I saw this: https://github.com/jankais3r/LLaMA_MPS
it runs slightly slower on the GPU than under llama.cpp but uses much less power doing so
I would guess the slowness is due to immaturity of the PyTorch MPS backend, the asitop graphs show it doing a bunch of cpu along with the gpu, so it might be inefficiently falling back to cpu for some ops and swapping layers back and forth (I have no idea, just guessing)
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Apples effort on developing Chat GPT like functions?
Not chatgpt, but also nothing to sneeze at. https://github.com/jankais3r/LLaMA_MPS 7B llm on 32gb m1 pro.
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llama VS LLaMA_MPS - a user suggested alternative
2 projects | 10 Mar 2023
llama-mps
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llama.cpp now officially supports GPU acceleration.
There are currently at least 3 ways to run llama on m1 with GPU acceleration. - mlc-llm (pre-built, only 1 model has been ported) - tinygrad (very memory efficient, not that easy to integrate into other projects) - llama-mps (original llama codebase + llama adapter support)
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LLaMA-7B in Pure C++ with full Apple Silicon support
There is also a gpu-acelerated fork of the original repo
https://github.com/remixer-dec/llama-mps
- Llama-CPU: Fork of Facebooks LLaMa model to run on CPU
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[D] Tutorial: Run LLaMA on 8gb vram on windows (thanks to bitsandbytes 8bit quantization)
I tried to port the llama-cpu version to a gpu-accelerated mps version for macs, it runs, but the outputs are not as good as expected and it often gives "-1" tokens. Any help and contributions on fixing it are welcome!
What are some alternatives?
m1xxx - Unofficial native Mixxx builds for macOS (Apple Silicon/Intel) and Linux
llama - Inference code for Llama models
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
RedPajama-Data - The RedPajama-Data repository contains code for preparing large datasets for training large language models.
awesome-ml - Curated list of useful LLM / Analytics / Datascience resources
vanilla-llama - Plain pytorch implementation of LLaMA
llama - Inference code for LLaMA models
Multi-Modality-Arena - Chatbot Arena meets multi-modality! Multi-Modality Arena allows you to benchmark vision-language models side-by-side while providing images as inputs. Supports MiniGPT-4, LLaMA-Adapter V2, LLaVA, BLIP-2, and many more!
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️
llama-dfdx - LLaMa 7b with CUDA acceleration implemented in rust. Minimal GPU memory needed!
llama-dl - High-speed download of LLaMA, Facebook's 65B parameter GPT model [UnavailableForLegalReasons - Repository access blocked]