Update README.md

README.md

@@ -1,13 +1,13 @@
----
-tags:
-- merge
-- mergekit
-- lazymergekit
----
 
-# AverageAdmixture
+# Average Normie v3.69
 
-AverageAdmixture is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+The third step in the Average Normie line sees a very big step toward NSFW content, while also allowing for steerability through example messages and first message editing. If you do not want an NSFW conversation, I highly recommend removing all NSFW content from your character card and examples, because this model will lean into that use case very heavily.
+
+With that said, I am seeing a fair bit of flexibility with first message editing and example message editing. This model will take on the speech pattern that you set for it, so don't be discouraged if you need to modify the first message to get a better chat style.
+
+The responses can be very human-like and impressive, and the model will gladly stick to any writing style you direct it to use. If you want shorter responses, then prompt it for shorter responses in your system prompt or character card. Don't be afraid to prompt the bot to alter its speech patterns, it is very flexible.
+
+I hope you guys like this model. Make sure your inference software is updated to the very latest version if you have any issues. Thanks for checking this one out!
 
 ## 🧩 Configuration
 
@@ -19,29 +19,4 @@ models:
 merge_method: model_stock
 base_model: saishf/Aura-Uncensored-OAS-8B-L3
 dtype: float16
-```
-
-## 💻 Usage
-
-```python
-!pip install -qU transformers accelerate
-
-from transformers import AutoTokenizer
-import transformers
-import torch
-
-model = "jeiku/AverageAdmixture"
-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"])
 ```