<p align="center"><font size="6"> <b>Very "Sydney-like"</b> </font></p>

athirdpath/Lambda-17b trained with a new strategy I call "PcDPO", or "Pulsed conservative Direct Performance Optimization".

First, the LoRA was trained for 0.5 epochs on the large athirdpath/alpaca_dpo_pairs dataset @ 5e-5 LR, .2 label smoothing.

Then, the same LoRA was preserved going into a second run, this time 2 epochs on athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW @ 1e-5 LR, .05 label smoothing.

Finally, another 0.15 (ran out of compute) epochs of athirdpath/alpaca_dpo_pairs @ 3e-7 LR, no label smoothing. (standard DPO)

The logic here is that the process used to create Iambe-RP-v3 could be done with one LoRA instead of merges, by establishing a strong baseline sense of the prompt format and the *distinction* between factual/obedient and incorrect/distracted. Then, the model is introduced to the actual goal dataset at a much lower noise and learning rate.This is where the actual magic happens, the model learns a voice, and it will still overfit slightly. Doing it in this order means the training can focus on the actual goal, and not the prompt format or correcting factual/layer-splice errors. Then, the hyper-low rate final pass undoes some of the overfitting, and also lacks label smoothing, so it will correct misunderstandings from earlier cDPO nonsense.

This is a proof of concept that didn't get enough compute, once my current financial woes end I'll be able to do an actual prototype.

{"language": ["en"], "license": "cc-by-nc-4.0", "datasets": ["athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW", "athirdpath/alpaca_dpo_pairs"]}
transformers safetensors mistral text-generation en dataset:athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW dataset:athirdpath/alpaca_dpo_pairs license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us