Update README.md

README.md

@@ -142,17 +142,16 @@ actions lead to what kind of consequenses.
 
 5) LLM deployment phase
 
-One of the main high lights of our System is LLM and digital human in the loop which is possible because of flexible RTG conditioning posible because of transformer architecture.
-Different RTG can lead to different sequences which is completely different approach where we give control to LLM and because of that we never face sudden uneven temperature settings.
-
-For LLM utilisation please download and setup OLLAMA 
-
-For this experiments we used several opensource Quantized versions of both resonsing and non reasoning LLMS liek DEEPseek V1 and r1 lamma, etc. 
-you have to create the llm client and server setup which is provided in the llm folder. 
+After this you have to move on to the inference Server side to tie up all these together
+Gen-HVAC supports an optional LLM + Digital Human-in-the-Loop (DHIL) layer that modulates preference/RTG targets and high-level
+constraints while the controller produces smooth setpoint sequences. For local LLM hosting, install Ollama, pull a quantized model
+, and launch the service.
 
-Our system use LLM to capture the humnistic value of the human behaviour so you can change or tweak the prompt as per your behaviour or create your own personal digital persona.
+On Linux/macOS you can install Ollama via curl -fsSL https://ollama.com/install.sh | sh,  start the daemon with ollama serve (leave it running), and pull recommended models using ollama pull deepseek-r1:7b (lightweight reasoning), ollama pull llama3.1:8b (strong general instruction-following), ollama pull qwen2.5:7b (efficient general model), or ollama pull mistral:instruct (fast instruct model). If you want a slightly heavier but still practical model, ollama pull deepseek-r1:14b or ollama pull qwen2.5:14b.
+In our testing we choose Deepseek R1.
 
-After this you have to move on to the inference Server side to tie up all these together
+Once pulled, sanity-check locally with ollama run deepseek-r1:7b, then in another terminal point your Gen-HVAC LLM client to the default endpoint  and run your integration from the llm/ folder (e.g., python -m llm.server --host 0.0.0.0 --port 8000 and python -m llm.client --base_url http://localhost:xxxx --model deepseek-r1:7b.
+After the LLM endpoint is up, you can proceed to the inference server step to bind the persona/prompt layer to RTG conditioning and the control loop in one end to end pipeline.
 
 7) Inference