peft
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. (by huggingface)
LyCORIS
Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion. (by KohakuBlueleaf)
| peft | LyCORIS | |
|---|---|---|
| 26 | 13 | |
| 13,877 | 1,972 | |
| 4.1% | - | |
| 9.7 | 9.6 | |
| 4 days ago | 2 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
peft
Posts with mentions or reviews of peft.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-05.
- 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.
LyCORIS
Posts with mentions or reviews of LyCORIS.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-28.
-
LoRA (LyCORIS) iA3 is amazing (info in 1st comment)
Lycoris is another implementation of LoRA done by KohakuBlueleaf: https://github.com/KohakuBlueleaf/LyCORIS
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Training LORAs locally guide in text form?
Most guides focus on LoRa training as that has been around for longer. But I think LoHa can give better results. But the training is about half as fas it/s and it requires different training settings.
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Guide to DreamBooth / LORA / LyCORIS
I've read in some tutorials that it is best that the value should be 64 or below, also here they suggest to not go over 64 ( https://github.com/KohakuBlueleaf/LyCORIS )
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LyCORIS doesn't work with inpainting models
Does anyone know how to make LyCORIS models (https://github.com/KohakuBlueleaf/LyCORIS) work with inpainting models?
- wtf is a lycoris?
- I wonder what to do with this?
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I'm the creator of LoRA. How can I make it better?
I think it was linked already but this is also relevant for LoRa: https://github.com/KohakuBlueleaf/LyCORIS Nice work!
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LoRA: Low-Rank Adaptation of Large Language Models
There are some WIP evolutions of SD Lora in the works, like locon and lycoris.
https://github.com/KohakuBlueleaf/LyCORIS
- What the hell is a Locon/Loha model?
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SD fine-tuning methods compared: a benchmark
You might want to expand LoRA to include LoCon and LoHa, (and also add a column for VRAM requirements) (Think of it as a more complete LoRA that works for the kernels in the convolutional units rather than just the weights for the feed-forward network), support is still quite limited, but it's starting to pick up steam https://github.com/KohakuBlueleaf/LyCORIS
What are some alternatives?
When comparing peft and LyCORIS you can also consider the following projects:
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
alpaca-lora - Instruct-tune LLaMA on consumer hardware
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
dalai - The simplest way to run LLaMA on your local machine
sd-webui-additional-networks
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
kohya_ss
minLoRA - minLoRA: a minimal PyTorch library that allows you to apply LoRA to any PyTorch model.
StableTuner - Finetuning SD in style.