LLaMA_MPS
peft
| LLaMA_MPS | peft | |
|---|---|---|
| 4 | 26 | |
| 566 | 13,877 | |
| - | 4.1% | |
| 10.0 | 9.7 | |
| about 1 year ago | 2 days ago | |
| Python | Python | |
| GPL-3.0 | Apache License 2.0 |
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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
peft
- LoftQ: LoRA-fine-tuning-aware Quantization
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Fine Tuning Mistral 7B on Magic the Gathering Draft
There is not a lot of great content out there making this clear, but basically all that matters for basic fine tuning is how much VRAM you have -- since the 3090 / 4090 have 24GB VRAM they're both pretty decent fine tuning chips. I think you could probably fine-tune a model up to ~13B parameters on one of them with PEFT (https://github.com/huggingface/peft)
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Whisper prompt tuning
Hi everyone. Recently I've been looking into the PEFT library (https://github.com/huggingface/peft) and I was wondering if it would be possible to do prompt tuning with OpenAI's Whisper model. They have an example notebook for tuning Whisper with LoRA (https://colab.research.google.com/drive/1vhF8yueFqha3Y3CpTHN6q9EVcII9EYzs?usp=sharing) but I'm not sure how to go about changing it to use prompt tuning instead.
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Code Llama - The Hugging Face Edition
In the coming days, we'll work on sharing scripts to train models, optimizations for on-device inference, even nicer demos (and for more powerful models), and more. Feel free to like our GitHub repos (transformers, peft, accelerate). Enjoy!
- PEFT 0.5 supports fine-tuning GPTQ models
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Exploding loss when trying to train OpenOrca-Platypus2-13B
image
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[D] Is there a difference between p-tuning and prefix tuning ?
I discussed part of this here: https://github.com/huggingface/peft/issues/123
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How does using QLoRAs when running Llama on CPU work?
It seems like the merge_and_unload function in this PEFT script might be what they are referring to: https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora.py
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How to merge the two weights into a single weight?
To obtain the original llama model, one may refer to this doc. To merge a lora model with a base model, one may refer to PEFT or use the merge script provided by LMFlow.
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[D] [LoRA + weight merge every N step] for pre-training?
you could use a callback, like show here, https://github.com/huggingface/peft/issues/286 and call code to merge them here.
What are some alternatives?
llama-mps - Experimental fork of Facebooks LLaMa model which runs it with GPU acceleration on Apple Silicon M1/M2
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
m1xxx - Unofficial native Mixxx builds for macOS (Apple Silicon/Intel) and Linux
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
alpaca-lora - Instruct-tune LLaMA on consumer hardware
RedPajama-Data - The RedPajama-Data repository contains code for preparing large datasets for training large language models.
dalai - The simplest way to run LLaMA on your local machine
vanilla-llama - Plain pytorch implementation of LLaMA
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!
minLoRA - minLoRA: a minimal PyTorch library that allows you to apply LoRA to any PyTorch model.