ainodes-engine VS alpaca-lora

currently working on a pyqt node based app, qt has a lot of 3d power. XmYx/ainodes-engine (github.com)

1: git clone -b comfy-dev https://github.com/XmYx/ainodes-engine 2: cd ainodes-engine 3: setup_ainodes.bat then you can start ainodes the usual way, but also ComfyUI with run_comfyui.bat. If you install any custom node package for comfy using the comfy manager, those get imported into the desktop engine on next start. Compatibility is WIP, but most image and sampling nodes will work. (Disco Diffusion too) To install the Comfy Manager run the following from ainodes-engine folder: 1: cd src 2: cd ComfyUI 3: cd custom_nodes 4: git clone https://github.com/ltdrdata/ComfyUI-Manager.git

XmYx/ainodes-engine (github.com) patreon.com/deforum_ainodes

And take a look at the node engine if you are interested: https://github.com/XmYx/ainodes-engine

You can see some of our previous open source works at: XmYx/ainodes-engine (github.com)

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