Update README.md

README.md

@@ -56,3 +56,78 @@ model:
 
   custom_model = DynamicNeuralNetwork.from_pretrained("ayjays132/phillnet", config=DynamicNeuralNetworkConfig.from_pretrained("ayjays132/phillnet"))
 ---
+
+# Model Card for Phillnet 🚀
+
+## Model Overview
+
+Phillnet, a marvel in the realm of language models, is a cutting-edge Dynamic Neural Network designed for advanced natural language processing tasks. Breaking away from conventional models, Phillnet exhibits dynamic adaptation and continuous evolution, showcasing its prowess in continual improvement. Crafted with a custom architecture, Phillnet seamlessly integrates an Innovative Growth Network, ushering in adaptability and innovation.
+
+## Key Features
+
+- **Model Type:** Dynamic Neural Network 🧠
+- **Embedding Dimension:** 768
+- **Hidden Dimension:** 2048
+- **Initial Neuron Count:** 5000
+- **Input Dimension:** 768
+- **Max Neurons:** 250000
+- **Max Sequence Length:** 200
+- **Num Embeddings:** 25000
+- **Model Filename:** pytorch_model.bin
+- **Transformers Version:** 4.34.0
+
+## Ecosystem Dynamics 🌐
+
+Phillnet thrives in a dynamic ecosystem:
+
+- **Environmental Volatility:** 0.1
+- **Resource Pool:** 1.0
+
+## Innovative Growth Network 🌱
+
+Empowered by an Innovative Growth Network for dynamic adaptation:
+
+- **Adaptation Rate:** 0.05
+- **Initial Capacity:** 250000
+- **Input Size:** 2048
+
+## Seamless Integration with Hugging Face 🤗
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("ayjays132/phillnet")
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+model = AutoModelForCausalLM.from_pretrained("ayjays132/phillnet")
+
+# Example conversation
+conversation_history = [
+    "Hello, how are you?",
+    "I'm doing well, thank you! How about you?",
+    "I'm good too. What's new with you?",
+    "Not much, just working on some projects. How can I help you today?"
+]
+
+# Concatenate the conversation strings
+conversation_text = " ".join(conversation_history)
+
+# Tokenize and pad the input
+input_ids = tokenizer.encode(conversation_text, return_tensors="pt", padding=True, truncation=True)
+
+# Generate a response
+output_ids = model.generate(input_ids, max_length=150, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
+
+# Decode the generated response
+generated_response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+# Print the generated response
+print("Generated Response:", generated_response)
+```
+
+## Experience the Magic ✨
+
+- **Adaptive Learning:** Phillnet dynamically adapts to data patterns, continually enhancing its performance.
+- **Innovative Growth:** The model evolves through an Innovative Growth Network, ensuring continuous enhancement.
+- **Custom Architecture:** Crafted with a dynamic custom architecture for unparalleled language understanding.
+
+---