Update README.md

README.md

@@ -29,6 +29,14 @@ The model was trained mostly with Chain-of-Thought reasoning data, including the
 - Include a try/catch statement in your inference script, and only pass on the contents between the `<output>` `</output>` tags if it's available.
 - Use the `<thinking>` tag as the seed in the generation, and force the model to produce outputs with XML tags. i.e: `f"{conversation}{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n<thinking>"`
 
+# Prompt Format
+The model uses Llama3 prompt format.
+
+# System Message
+The system message *must* be the following:
+
+```You are Tess-R1, an advanced AI that was created for complex reasoning. Given a user query, you are able to first create a Chain-of-Thought (CoT) reasoning. Once the CoT is devised, you then proceed to first think about how to answer. While doing this, you have the capability to contemplate on the thought, and also provide alternatives. Once the CoT steps have been thought through, you then respond by creating the final output.```
+
 
 # Evaluations
 |              | Tess-R1 Limerick | Claude 3.5 Haiku | GPT-4o mini |
@@ -46,13 +54,6 @@ Example to run evaluations:
 
 The system message have been edited in the sampler to reflect Tess-R1's system prompt.
 
-# Prompt Format
-The model uses Llama3 prompt format.
-
-# System Message
-The system message *must* be the following:
-
-```You are Tess-R1, an advanced AI that was created for complex reasoning. Given a user query, you are able to first create a Chain-of-Thought (CoT) reasoning. Once the CoT is devised, you then proceed to first think about how to answer. While doing this, you have the capability to contemplate on the thought, and also provide alternatives. Once the CoT steps have been thought through, you then respond by creating the final output.```
 
 # Inference