peft VS alpaca_lora_4bit

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)

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.

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!

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I discussed part of this here: https://github.com/huggingface/peft/issues/123

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

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.

you could use a callback, like show here, https://github.com/huggingface/peft/issues/286 and call code to merge them here.

For training there is also https://github.com/johnsmith0031/alpaca_lora_4bit

I've been trying to fine tune an erotica model on some large context chat history (reverse proxy logs) and a literotica-instruct dataset I made, with a max context of 8k. The large context size eats a lot of VRAM so I've been trying to find the most efficient way to experiment considering I'd like to do multiple runs to test some ideas. So I'm going to try and use https://github.com/johnsmith0031/alpaca_lora_4bit, which is supposed to train faster and use less memory than qlora.

Follow up the popular work of u/tloen alpaca-lora, I wrapped the setup of alpaca_lora_4bit to add support for GPTQ training in form of installable pip packages. You can perform training and inference with multiple quantizations method to compare the results.

" Using LoRAs with GPTQ-for-LLaMa This requires using a monkey patch that is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit"

4-bit GPTQ LoRA training was available since early April. I did not see any comparison to it in the QLoRA paper or even a mention, so it makes me think they were not aware it already existed.

https://github.com/johnsmith0031/alpaca_lora_4bit is still the SOTA - Faster than qlora, trains on a GPTQ base.

I would also like to know how one would finetune this in 4 bit? I think one could take the merged 8K PEFT with the LLaMA weights, and then quantize it to 4 bit, and then train with https://github.com/johnsmith0031/alpaca_lora_4bit ?

I was under the impression that you just git clone this repo into text-generation-webui/repositories (so you would have GPTQ_for_Llama and alpaca_lora_4bit in the folder), and then just load with monkey patch. Is that not correct? I also tried just downloading alpaca_lora_4bit on its own, git cloning text-gen-webui within it, and installing requirements.txt for both and running with monkey patch. I was following the sections of alpaca_lora_4bit, "Text Generation Webui Monkey Patch" and "monkey patch inside webui"

I dont have any familiarity with esxi, but I can say that there are quite a few posts about people doing it on proxmox. I've currently got a machine with 2x3090 passing through to VM's. When I'm training, I pass them both through to the same VM and can do lora 4-bit training on llama33 using https://github.com/johnsmith0031/alpaca_lora_4bit. Then, at inference time, I run a single card into a different VM, and have an extra card available for experimentation.