Update README.md

README.md

@@ -50,13 +50,60 @@ parameters:
 dtype: bfloat16
 ```
 
-### Key Parameters
+### 🔑 Key Parameters
 
 - **Self-Attention Filtering** (`self_attn`): Controls the blending extent across self-attention layers, allowing for a dynamic mix between the two source models.
 - **MLP Filtering** (`mlp`): Adjusts the balance within the Multi-Layer Perceptrons, fine-tuning the model’s neural network layers for optimal performance.
 - **Global Weight (`t.value`)**: Sets a general interpolation factor for all unspecified layers, ensuring an equal contribution from both models.
 - **Data Type (`dtype`)**: Utilizes `bfloat16` to maintain computational efficiency while preserving high precision.
 
+### 🗣️ Inference
+
+Below is an example of how to load and use the model for text generation:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+# Define the model name
+model_name = "ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B"
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Load the model
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Initialize the pipeline
+text_generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Define the input prompt
+prompt = "Explain the significance of artificial intelligence in modern healthcare."
+
+# Generate the output
+outputs = text_generator(
+    prompt,
+    max_new_tokens=150,
+    do_sample=True,
+    temperature=0.7,
+    top_k=50,
+    top_p=0.95
+)
+
+# Print the generated text
+print(outputs[0]["generated_text"])
+```
+
 ## 🎯 Use Case & Applications
 
 **Qwen-2.5-Aether-SlerpFusion-7B** excels in scenarios that require both robust language understanding and specialized task performance. This merged model is ideal for: