Update README.md

README.md

@@ -31,30 +31,14 @@ Inspired by and featuring the Reflection Tuning technique (Published by Matt Shu
 
 From the author of the first "reflection tuned" Llama 3.1 8B LLM.
 
-As per one of the inspiring model "mattshumer/Reflection-Llama-3.1-70B" (this model was not used in the training process nor as a foundational model, but only served as inspiration) :
+Due to the method fo data structuring and training implemented in this model fine-tune, the following 
 
-"""
-
-During sampling, the model will start by outputting reasoning inside and tags, and then once it is satisfied with its reasoning, it will output the final answer inside and tags. Each of these tags are special tokens, trained into the model.
-
-This enables the model to separate its internal thoughts and reasoning from its final answer, improving the experience for the user.
+As per one of the inspiring model "mattshumer/Reflection-Llama-3.1-70B" (this model was not used in the training process nor as a foundational model, but only served as inspiration) this model may benefit from this master prompt:
 
-Inside the section, the model may output one or more tags, which signals the model has caught an error in its reasoning and will attempt to correct it before providing a final answer.
-
-System Prompt: The system prompt used for training this model is:
+"""
 
 You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside tags, and then provide your final response inside tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside tags.
 
-We recommend using this exact system prompt to get the best results from Reflection Llama-3.1 70B. You may also want to experiment combining this system prompt with your own custom instructions to customize the behavior of the model.
-
-Chat Format: As mentioned above, the model uses the standard Llama 3.1 chat format. Here’s an example:
-
-<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-
-You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside tags, and then provide your final response inside tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside tags.<|eot_id|><|start_header_id|>user<|end_header_id|>
-
-what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
-
 """
 
 **Example of this model in action:**