---
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- en
base_model:
- LiquidAI/LFM2.5-1.2B-Instruct
pipeline_tag: text-generation
tags:
- rp
- roleplay
- creative
- writer
- finetune
- lora
- NSFW
- ERP
- V2
- Super
---

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

![Super slop machina v2](https://cdn-uploads.huggingface.co/production/uploads/6a1a2287b0fa00c11077d9fb/LVpH60Ow7GLlXw7wjL8wU.png)

"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.