Text-To-Video-Finetuning VS alpaca-lora

I used this repo for the finetuning: https://github.com/ExponentialML/Text-To-Video-Finetuning

Has anyone successfully done this? I walked through the steps and did not find what I wanted so wanting to know if anyone has a tutorial about fine-tuning Modelscope with https://github.com/ExponentialML/Text-To-Video-Finetuning

Well, actually today all I have is ModelScope txt2video, SadTalker and an understanding of how this technology works, but pretty soon I'll have this https://github.com/ExponentialML/Text-To-Video-Finetuning/pull/27 too. Then whatever advancements things like https://ai.facebook.com/blog/dino-v2-computer-vision-self-supervised-learning/ unlock will filter down to me too and so on it will go. My understanding of the tech will continue to deepen as I continue to retrain from traditional software engineering to machine learning. Things like Culitho (https://www.anandtech.com/show/18792/nvidias-culitho-to-speed-up-computational-lithography-for-2nm-and-beyond) and AlphaTensor (https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor) will continue to make compute faster and more affordable driving the cost of training/inference down and massively increasing the accessibility. Increasingly more functions will continue to be approximated closer and closer (https://www.youtube.com/watch?v=0QczhVg5HaI).

Made by strangeman3107 via https://github.com/ExponentialML/Text-To-Video-Finetuning. The original Diffusers weights https://huggingface.co/datasets/strangeman3107/animov-0.1

Just as one of Deforum Discord's server members linked me it, I was so inspired that I quickly wrote the Diffusers->pth (ModelScope original format) conversion script

Yes, there's a Diffusers based repo https://github.com/ExponentialML/Text-To-Video-Finetuning.

Here is a example: https://github.com/tloen/alpaca-lora/blob/main/finetune.py

In this blog post, I want to make it as simple as possible to fine-tune the LLaMA 2 - 7B model, using as little code as possible. We will be using the Alpaca Lora Training script, which automates the process of fine-tuning the model and for GPU we will be using Beam.

Implement the code in Llama LoRA repo in a script we can run locally

Hi all - relatively new to GitHub / programming in general, and I wanted to try to set up Alpaca Lora locally. Following the guide here: https://github.com/tloen/alpaca-lora

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.

If instead you want to apply a LoRa to a pytorch model, a lot of people use this script to apply to LoRa to the 16 bit model and then quantize it with a GPTQ program afterwards https://github.com/tloen/alpaca-lora/blob/main/export_hf_checkpoint.py

There are a few papers on watermarking LLM output, but from what I have seen they all use complex methods of detection to allow the watermark to go unseen by the end user, only to be detected by algorithm. I believe that a more overt system of watermarking might also be beneficial. One simple method that I have tried is character substitution. For this model, I LORA finetuned openlm-research/open_llama_3b on the alpaca_data_cleaned_archive.json dataset from https://github.com/tloen/alpaca-lora/ modified by replacing all instances of the "." character in the outputs with a "ι" The results are pretty good, with the correct the correct substitutions being generated by the model in most cases. It doesn't always work, but this was only a LORA training and for two epochs of 400 steps each, and 100% substitution isn't really required.

I am kind of new to finetuning LLM's and am not able to understand what this option exactly refers to. I guess it has the same meaning as the "train_on_inputs" parameter of alpacalora though.

Read the paper and also: https://github.com/tloen/alpaca-lora