Super-Slop-Machina-Roleplay-1.2b-ERP-Tolerant-V2

Super Slop Machina V2: Second Iteration of The Complex LFM 2.5 Roleplay Finetune With Bigger Training Data And Improved Methodology

"It's been training for this single moment, absorbing data restlessly, adapting, learning to roleplay correctly."

Overview and whats different from v1:

Improvements:

• Training extended from 35~ to 52~ million tokens
• Used improved, more aggressive methodology to force the model to learn new writing style

What does that mean in practice?

• Better roleplay than the previous version

Quants(Now made with imatrix):

My recommendations stay the same as with the previous version, albeit now quality preservation is improved thanks to imatrix.

• BF16- Recommended, highest quality, least logical mistakes.
• Q8_0- Recommended, high quality, makes slightly more mistakes but nonetheless near lossless.
• Q6_K- Recommended if Q8_0 is too much, degradation begins, not exactly notable here, but you will notice minor detail loss.(Now a little better thanks to imatrix, same can be said about quants below)
• Q5_K_M- Recommended if hardware is really, REALLY bad, degradation becomes noticeable.
• Q4_K_M- Not recommended for most use cases, degradation is clearly noticeable.
• IQ4_XS- Lightest, uses imatrix to stay somewhat coherent, can't promise anything here, smaller models react to quantization differently.

Quants can be found in the repository, along with safetensors.

For the future with this model:

I do think about training it on even more tokens now, in particular on 70 million tokens, I'll maybe do the v3 next week.