Versions 1 and 2 were the most verbose models I'd ever merged but they had lost most of their NSFW MoJo. I think I finally got the mix right as v3 returns some of the most NSFW I've seen...

Over a dozen models and at least 25 dataset were involved in this merge. Eros_Scribe-10.7b-v3 is a merge of the following models:

OmnicromsBrain/EverythingBagel-DPO-7B

jondurbin/bagel-dpo-7b-v0.5

SanjiWatsuki/Silicon-Maid-7B

chargoddard/loyal-piano-m7

NeverSleep/Noromaid-7b-v0.2

athirdpath/NSFW_DPO_vmgb-7b

xDAN-AI/xDAN-L1-Chat-RL-v1

OmnicromsBrain/ToppyCox-7B

N8Programs/Coxcomb

Undi95/Toppy-M-7B

openchat/openchat_3.5

NousResearch/Nous-Capybara-7B-V1.9

HuggingFaceH4/zephyr-7b-beta

Undi95/zephyr-7b-beta-pippa-sharegpt

Undi95/Nous-Capybara-7B-V1.9-120-Days

Undi95/openchat_3.5-LimaRP-13B

lemonilia/AshhLimaRP-Mistral-7B

mistralai/Mistral-7B-v0.1

⚡ Quantized Models

Special thanks to MRadermacher for the static and imatrix quantized models

.GGUF https://huggingface.co/mradermacher/Eros_Scribe-10.7b-v3-GGUF

IMatrix https://huggingface.co/mradermacher/Eros_Scribe-10.7b-v3-i1-GGUF

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "OmnicromsBrain/Eros_Scribe-10.7b-v3"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])