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Alsebay/
My_LLMs_Leaderboard

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This is the Leaderboard about ranking my own model :) Also some useful information (Maybe). Main purpose is for Roleplay

Rank	Name	Parameter	Context Length	Tag	Note
💎1	Narumashi-RT	11B	4K	Lewd	Good for Roleplay, although it is LLAMA2. Thank Sao10k :) Could handle some (limited) TSF content.
🏆2	NaruMoE	3x7B	8K - 32K	Neurral	AVG model, could only handle limited extra content I want.
✌3	NarumashiRTS	7B	8K	Neurral	Base on Kunoichi-7B, so it good enough. Know the extra content. Not lewd and will skip lewd content sometime.
4	HyouKan Series	3x7B	8K - 32K	Neurral	ATTENTION: DON'T USE GGUF VERSION SINCE IT HAVE SOME BUGS (VARY BY VERSION) All-rounded Roleplay model. Understand well Character Card and good logic. The first version have 8k context lenght.
5	SunnyRain	2x10.7B	4K	Lewd	To be real, it perform approximate like HyouKan in Roleplay, just got some strange behavious.
6	RainyMotip	2x7B	32K	Neurral	Good enough model, ok in Roleplay.
7	Nutopia	7B	32K	Not for Roleplay	I don't think this work for Roleplay, but it good for solving problem
8	TripedalChiken	2x7B	32K	Not for Roleplay	Solving problem is good, but for Roleplay, I don't think so

Lewd : perform well NSFW content. Some of lewd words will appear in normal content if your Character Card have NSFW informations.
Neurral : perform well SFW content, can perform well NSFW content (limited maybe). Lewd words will less appear in chat/roleplay than Lewd
Not for Roleplay : seem that those model with this tag not understand well Character Card. But its logical is very good.
RT: Rough Translation Dataset that could lead to worse performance than original model.
CN: Chinese dataset pretrain, maybe not understand extra content in English. (I can't find any good english verion.)

The Context Length affect too much to your Memory. Let's say I have 16GB Vram card, I can run the model in 2 ways, using Text-Generation-WebUI:
1. Inference: download the origin model, apply args: --load-in-4bit --use_double_quant. I can run all of my model in leaderboard. The bigger parameter is, the slower token can generate. (Ex:7B model could run in 15 token/s, since 3x7b model could only run in ~4-5 token/s)
2. GGUF Quantization (Fastest,cheapest way to run): After you downloaded GGUF version of those models, sometimes, you can't run it although you can run other model that have bigger parameter. That because:
  - The context length: 16GB VRAM GPU could run maximum 2x10.7B (~ 19.2B) model with 4k context length. (5 token/s)
  - That model is bug/broken.😏
Bigger model will have more information that you need for your Character Card.
Best GGUF version that you should run (balance speed/performance): Q4_K_M, Q5_K_M (Slower than Q4)