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license: mit

SuperHOT Prototype 2 w/ 4-8K Context

This is a second prototype of SuperHOT, a NSFW focused LoRA, this time with 4K context and no RLHF. In my testing, it can go all the way to 6K without breaking down and I made the change with intention to reach 8K, so I'll assume it will go to 8K although I only trained on 4K sequences.

Looking for Merged & Quantized Models?

Using the monkey-patch?

You will NEED to apply the monkeypatch or, if you are already using the monkeypatch, change the scaling factor to 0.25 and the maximum sequence length to 8192

Using Oobabooga with Exllama?

  • python server.py --max_seq_len 8192 --compress_pos_emb 4 --loader exllama_hf

In order to use the 8K context, you will need to apply the monkeypatch I have added in this repo or follow the instructions for oobabooga's text-generation-webui -- without it, it will not work.

I will repeat: Without the patch with the correct scaling and max sequence length, it will not work!

The patch is very simple, and you can make the changes yourself:

  • Increase the max_position_embeddings to 8192 to stretch the sinusoidal
  • Stretch the frequency steps by a scale of 0.25

The intuition is to calibrate the model to within the learned positions of the pre-trained model as the model may be overfit on the token-position relationship (not my idea, Ofir Press'). By interpolating the encodings, we remain within the bounds of the pre-trained model (work with the overfitting rather than against it). The monkeypatch will work for the pre-trained model without fine-tuning, but you will need to fine-tune as the results will not be that good without it.

It can probably be even better than this with a few other modifications which I am testing (swap softmax for ReLU, increase head dimension)

In my testing, I tried random positional encoding, but I was not able to replicate the results of Jianlin Su, so maybe I did it incorrectly. I also tried shifted positions, log n scaling, log-sigmoid, and increase the head dimension, though this dilated RoPE (DoPE :) ) is the only one which worked for me consistently -- Note these are all based on finetuning, since the goal is to extend the context of the pre-trained model. Pre-training will paint a different picture.

I trained the LoRA with the following configuration:

  • 1200 samples (~400 samples over 2048 sequence length)
  • learning rate of 3e-4
  • 3 epochs
  • The exported modules are:
    • q_proj
    • k_proj
    • v_proj
    • o_proj
    • all bias
  • Rank = 2
  • Alpha = 8
  • no dropout
  • weight decay of 0.1
  • AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
  • Trained on 4-bit base model