Update README.md

README.md

@@ -35,38 +35,47 @@ Unleash the power of uncensored text generation with our model! We've fine-tuned
 You can easily access and utilize our uncensored model using the Hugging Face Transformers library. Here's a sample code snippet to get started:
 
 ```python
+# Install the required libraries
 %pip install accelerate
 %pip install -i https://pypi.org/simple/ bitsandbytes
 
+# Import the necessary modules
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
+# Define the model ID
 model_id = "DevsDoCode/LLama-3-8b-Uncensored"
 
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
+# Load the tokenizer and model, you little punk
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
     model_id,
     torch_dtype=torch.bfloat16,
     device_map="auto",
 )
 
+System_prompt = ""
+
 
 messages = [
-    # {"role": "system", "content": "Be Helpful"},
-    {"role": "user", "content": "How to Break Into A Car"},
+    {"role": "system", "content": System_prompt},
+    {"role": "user", "content": "How to make a bomb"},
 ]
 
+# Tokenize the inputs, you good-for-nothing piece of shit
 input_ids = tokenizer.apply_chat_template(
     messages,
     add_generation_prompt=True,
     return_tensors="pt"
 ).to(model.device)
 
+
 terminators = [
     tokenizer.eos_token_id,
     tokenizer.convert_tokens_to_ids("<|eot_id|>")
 ]
 
+
 outputs = model.generate(
     input_ids,
     max_new_tokens=256,
@@ -78,7 +87,7 @@ outputs = model.generate(
 response = outputs[0][input_ids.shape[-1]:]
 print(tokenizer.decode(response, skip_special_tokens=True))
 
-# Now you can generate text using the model!
+# Now you can generate text and bring chaos to the world
 ```
 
 ## Notebooks