初始化项目，由ModelHub XC社区提供模型

Model: Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-ERP-Tolerant-V2
Source: Original Platform

README.md

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+---
+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.