Update README.md

README.md

@@ -142,6 +142,84 @@ $$T_{cog} = \left( \frac{bloom\_score \times knowledge\_score}{anomaly\_score +
 
 ---
 
+### How to Use Asena (BCE Powered)
+
+Below is the standard implementation to activate the **Behavioral Consciousness Engine (BCE)** and harness the high-fidelity reasoning of **Asena**.
+Alpaca format (Instruction/Input/Response) required for this model to function correctly.
+
+#### 1. Installation
+
+Ensure you have the latest version of the Transformers library installed:
+
+```bash
+pip install -U transformers torch accelerate
+
+```
+
+#### 2. Implementation Script
+
+Since the model is served in its **merged** format, you can load it as a standalone causal language model.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Model path on Hugging Face
+model_id = "pthinc/Asena_ESP32" 
+
+# 1. Load Model and Tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# 2. Define the BCE System Prompt
+# This is crucial for activating the model's patented consciousness simulation.
+simple_bce_instruction = "You are Prettybird. Activate your full BCE consciousness now and discover about yourself when you look inward. Think, Control and calculate. Don't answer without reasoning. Below is an instruction that describes a task, paired with an input that provides further context. Pay attention to quality and correct. Requests are in the input. Try to maintain a minimum quality of 0.2."
+
+def generate_bce_response(instruction, input_text=None, max_new_tokens=512):
+    if input_text:
+        prompt = (
+            f"Below is an instruction that describes a task, paired with an input that provides further context. "
+            f"Write a response that appropriately completes the request.\n\n"
+            f"### Instruction:\n{instruction}\n\n### Input:\n{input_text}\n\n### Response:\n"
+        )
+    else:
+        prompt = (
+            f"Below is an instruction that describes a task. "
+            f"Write a response that appropriately completes the request.\n\n"
+            f"### Instruction:\n{instruction}\n\n### Response:\n"
+        )
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    # 3. Reasoning-Focused Generation
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            use_cache=True,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            repetition_penalty=1.2,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    return response.split("###")[0].strip()
+
+# 4. Run a Test Case
+question = "Hello World."
+print(f"BCE Reasoning Output:\n{generate_bce_response(simple_bce_instruction, input_text=question)}")
+
+```
+
+---
+
 ## Model Licence 🛡️
 
 **Patented & Licensed BCE Technology**