Update README.md

README.md

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----
-license: apache-2.0
----
+---
+activation_function: gelu
+architectures:
+  - DynamicNeuralNetwork
+attn_pdrop: 0.1
+bos_token_id: 50256
+embd_pdrop: 0.1
+eos_token_id: 50256
+initializer_range: 0.02
+layer_norm_epsilon: 1e-5
+model_type: phillnet1
+n_ctx: 512
+n_embd: 1024
+n_experts: 16
+n_layer: 1
+n_positions: 512
+n_special: 0
+predict_special_tokens: true
+task_specific_params:
+  conversational:
+    max_length: 512
+    min_length: 20
+    length_penalty: 1.5
+    num_beams: 5
+    early_stopping: true
+    no_repeat_ngram_size: 3
+    temperature: 0.7
+    top_k: 50
+    top_p: 0.9
+license: apache-2.0
+datasets:
+  - ayjays132/Sprout-AGI
+language:
+  - en
+tags:
+  - conversational
+  - dynamic
+  - adaptive
+metrics:
+  - perplexity
+  - accuracy
+custom_params:
+  adaptation_rate: 0.01
+  complexity_metric: null
+  growth_improvement_threshold: 0.01
+  hidden_dim: 1024
+  initial_neuron_count: 4096
+  innovative_growth_net:
+    adaptation_rate: 0.01
+    complexity_metric: null
+    initial_capacity: 4096
+    input_size: 2048
+  input_dimension: 1024
+  low_stability_threshold: 0.01
+  max_complexity: 50000
+  max_neurons: 4096
+  max_sequence_length: 512
+  min_epochs_before_growth: 5
+  model_filename: pytorch_model.bin
+  num_embeddings: 50280
+  pruning_improvement_threshold: 0.005
+  stability_threshold: 0.02
+  start_token_index: 2
+max_input_length: 512
+max_total_tokens: 515
+max_concurrent_requests: 128
+max_best_of: 2
+max_stop_sequences: 4
+max_top_n_tokens: 5
+waiting_served_ratio: 1.2
+max_batch_prefill_tokens: 512
+max_waiting_tokens: 200
+---
+
+<style>
+/* General Styles */
+@import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;600;800&display=swap');
+
+body {
+  font-family: 'Montserrat', sans-serif;
+  background-color: #121212;
+  margin: 0;
+  padding: 20px;
+  line-height: 1.6;
+  color: #e0e0e0;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  min-height: 100vh;
+  border-radius: 10px;
+  background: rgba(255, 255, 255, 0.05);
+}
+
+.container {
+  max-width: 1200px;
+  margin: 0 auto;
+  background: linear-gradient(145deg, rgba(20, 35, 55, 0.95), rgba(15, 25, 45, 0.9), rgba(10, 20, 40, 0.85));
+  padding: 60px;
+  border-radius: 35px;
+  box-shadow: 0 25px 70px rgba(0, 0, 0, 0.8), inset 0 0 25px rgba(255, 255, 255, 0.1);
+  position: relative;
+  overflow: hidden;
+  border: 2px solid rgba(100, 200, 255, 0.2);
+}
+.container::before {
+  content: '';
+  position: absolute;
+  top: -60%;
+  left: -60%;
+  width: 220%;
+  height: 220%;
+  background: radial-gradient(circle, rgba(255, 255, 255, 0.2), transparent);
+  animation: pulse 14s infinite;
+  pointer-events: none;
+}
+@keyframes pulse {
+  0% { transform: scale(1); }
+  50% { transform: scale(1.2); }
+  100% { transform: scale(1); }
+}
+.section {
+  margin-bottom: 70px;
+  position: relative;
+}
+.section:hover {
+  transform: translateY(-7px);
+  transition: all 0.5s ease-in-out;
+}
+.detail {
+  padding: 25px;
+  margin-bottom: 25px;
+  border: 1px solid rgba(120, 160, 220, 0.3);
+  border-radius: 20px;
+  background: linear-gradient(145deg, rgba(255, 255, 255, 0.1), rgba(100, 140, 200, 0.2));
+  box-shadow: 0 15px 35px rgba(0, 0, 0, 0.5), inset 0 0 15px rgba(255, 255, 255, 0.2);
+  transition: all 0.4s ease;
+}
+.detail:hover {
+  background: linear-gradient(145deg, rgba(255, 255, 255, 0.15), rgba(140, 180, 240, 0.25));
+  transform: translateY(-7px);
+  box-shadow: 0 20px 50px rgba(0, 0, 0, 0.7), inset 0 0 20px rgba(255, 255, 255, 0.25);
+}
+.detail-icon {
+  font-size: 1.8em;
+  color: #63d2ff;
+  margin-right: 20px;
+}
+.detail:hover .detail-icon {
+  color: #a2f4ff;
+  transform: scale(1.2);
+}
+ul {
+  list-style: none;
+  padding: 0;
+}
+ul li {
+  margin: 20px 0;
+  padding: 20px;
+  background: linear-gradient(145deg, rgba(255, 255, 255, 0.1), rgba(60, 100, 140, 0.25));
+  border-radius: 15px;
+  box-shadow: inset 0 0 15px rgba(0, 0, 0, 0.3), 0 8px 25px rgba(0, 0, 0, 0.6);
+  transition: all 0.4s ease;
+}
+ul li:hover {
+  background: linear-gradient(145deg, rgba(255, 255, 255, 0.15), rgba(80, 120, 160, 0.3));
+  transform: translateX(10px);
+  box-shadow: 0 15px 30px rgba(0, 0, 0, 0.5), inset 0 0 20px rgba(255, 255, 255, 0.2);
+}
+a {
+  color: #63d2ff;
+  text-decoration: none;
+  font-weight: bold;
+  transition: color 0.3s ease, text-shadow 0.3s ease;
+}
+a:hover {
+  color: #a2f4ff;
+  text-shadow: 0 0 12px rgba(255, 255, 255, 0.9), 0 0 18px rgba(100, 200, 255, 0.6);
+}
+h1, h2, h3 {
+  text-transform: uppercase;
+  color: #e8f0ff;
+  text-shadow: 5px 5px 15px rgba(0, 0, 0, 0.9), 0 0 20px rgba(255, 255, 255, 0.6);
+  font-weight: 700;
+}
+</style>
+
+<div class="container">
+  <h1 class="section-title">Welcome to ayjays132/PhillNet-1!</h1>
+
+  <div class="section">
+    <div class="section-header">
+      <h2 class="section-title">🎭 Distinctive Elements</h2>
+    </div>
+    <div class="section-content">
+      <div class="detail">
+        <div class="detail-icon">💬</div>
+        <div class="detail-text">Engaging Conversations: PhillNet 1 generates fluid, context-rich dialogue, continually evolving its internal state with every exchange.</div>
+      </div>
+      <div class="detail">
+        <div class="detail-icon">🧠</div>
+        <div class="detail-text">Dynamic Cognition: With its integrated LSTM core, MoE routing, and self-regulated learning, this model adapts in real-time to complex queries.</div>
+      </div>
+      <div class="detail">
+        <div class="detail-icon">⚡</div>
+        <div class="detail-text">Neuroevolution in Action: Utilizing an Innovative Growth Network, PhillNet 1 dynamically restructures its neurons to optimize performance.</div>
+      </div>
+    </div>
+  </div>
+
+  <div class="section">
+    <div class="section-header">
+      <h2 class="section-title">🛠️ Architectural Marvels</h2>
+    </div>
+    <div class="section-content">
+      <div class="detail">
+        <div class="detail-icon">🏛️</div>
+        <div class="detail-text">Dynamic Neural Core: Based on our DynamicNeuralNetwork class, PhillNet 1 features a 1024-dimensional embedding and hidden state, processing up to 512 tokens per sequence.</div>
+      </div>
+      <div class="detail">
+        <div class="detail-icon">🌀</div>
+        <div class="detail-text">Expert Routing: A Mixture-of-Experts layer with 16 experts and top-4 routing ensures specialized handling of varied content, driven by semantic and contextual cues.</div>
+      </div>
+      <div class="detail">
+        <div class="detail-icon">🎶</div>
+        <div class="detail-text">Self-Reflection & Growth: A dedicated Self-Regulated Learning module refines outputs before prediction, while an Innovative Growth Net continually adapts the network’s structure.</div>
+      </div>
+    </div>
+  </div>
+
+  <div class="section">
+    <div class="section-header">
+      <h2 class="section-title">📘 Core Training Dataset</h2>
+    </div>
+    <div class="section-content">
+      <p>Introducing the <strong>Core Reasoning Prime Dataset</strong>—a curated collection designed to train PhillNet 1 for advanced natural language understanding, ethical reasoning, and adaptive dialogue generation. Key features include:</p>
+      <ul>
+        <li><strong>Input:</strong> Rich, detailed prompts that encourage creative and logical responses.</li>
+        <li><strong>Context:</strong> Multi-modal context data to enhance memory and recall capabilities.</li>
+        <li><strong>Output:</strong> Expert-level responses refined by self-regulation and neuroevolution.</li>
+        <li><strong>Reasoning Type:</strong> Structured approaches that foster dynamic, adaptive intelligence.</li>
+      </ul>
+      <p>This dataset is pivotal in pushing PhillNet 1 beyond static language models, fostering continuous self-improvement and contextual awareness.</p>
+    </div>
+  </div>
+
+  <div class="section">
+    <div class="section-header">
+      <h2 class="section-title">🌐 Model Configurations</h2>
+    </div>
+    <div class="section-content">
+      <div class="detail">
+        <div class="detail-icon">📜</div>
+        <div class="detail-text">Activation & Initialization: Utilizing GELU activation with an initializer range of 0.02 and a layer norm epsilon of 1e-5 for stable training.</div>
+      </div>
+      <div class="detail">
+        <div class="detail-icon">🕰️</div>
+        <div class="detail-text">Adaptation Dynamics: An adaptation rate of 0.01 and a maximum neuron capacity of 4096 drive real-time neuroevolution.</div>
+      </div>
+      <div class="detail">
+        <div class="detail-icon">🌍</div>
+        <div class="detail-text">Sequence & Memory: Processes sequences of up to 512 tokens with a 1024-dimensional embedding space, integrating multi-level memory modules for contextual awareness.</div>
+      </div>
+    </div>
+  </div>
+
+  <div class="section">
+    <div class="section-header">
+      <h2 class="section-title">🔧 Hyperparameters Overview</h2>
+    </div>
+    <div class="section-content">
+      <p>Below is a concise overview of the key hyperparameters used to train PhillNet 1:</p>
+      <ul>
+        <li><strong>Max Neurons:</strong> 4096</li>
+        <li><strong>Embedding Dimension:</strong> 1024</li>
+        <li><strong>Hidden Dimension:</strong> 1024</li>
+        <li><strong>Initial Neuron Count:</strong> 4096</li>
+        <li><strong>Adaptation Rate:</strong> 0.01</li>
+        <li><strong>MoE Experts:</strong> 16 (Top-4 selected per token)</li>
+        <li><strong>Intermediate FFN Size:</strong> 2048</li>
+        <li><strong>Max Sequence Length:</strong> 512 tokens</li>
+        <li><strong>Vocabulary Size:</strong> 50280</li>
+      </ul>
+    </div>
+  </div>
+
+  <div class="section">
+    <div class="section-header">
+      <h2 class="section-title">🔗 Seamless Integration with Hugging Face</h2>
+    </div>
+    <div class="section-content">
+      <img src="https://huggingface.co/ayjays132/phillnet/resolve/main/Phillnet.png?download=true" alt="PhillNet 1 Model" style="width:100%; border-radius: 15px;">
+      <p>Load PhillNet 1 easily with the following script:</p>
+<pre>
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("ayjays132/PhillNet-1")
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+model = AutoModelForCausalLM.from_pretrained("ayjays132/PhillNet-1")
+
+# 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?",
+    "Working on innovative neuroevolution techniques—what about you?"
+]
+
+conversation_text = " ".join(conversation_history)
+input_ids = tokenizer.encode(conversation_text, return_tensors="pt", padding=True, truncation=True)
+output_ids = model.generate(input_ids, max_length=150, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
+generated_response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print("Generated Response:", generated_response)
+</pre>
+    </div>
+  </div>
+
+  <div class="section">
+    <div class="section-header">
+      <h2 class="section-title">💡 Experience the Magic</h2>
+    </div>
+    <div class="section-content">
+      <ul>
+        <li><strong>Adaptive Learning:</strong> PhillNet 1 continuously refines its internal state via self-regulated learning and neuroevolution.</li>
+        <li><strong>Innovative Growth:</strong> Real-time architecture adaptation enables dynamic neuron specialization.</li>
+        <li><strong>Contextual Awareness:</strong> Advanced memory modules integrate short-, episodic, and conceptual memories for rich contextual understanding.</li>
+      </ul>
+      <p>Welcome to a new era of AI—where every parameter evolves, every neuron thinks, and every token is a step toward true general intelligence.</p>
+    </div>
+  </div>
+  
+  <div class="section">
+    <div class="section-header">
+      <h2 class="section-title">📜 Usage and License</h2>
+    </div>
+    <div class="section-content">
+      <img src="https://huggingface.co/ayjays132/phillnet/resolve/main/usage.png?download=true" alt="Usage Example" style="width:100%; border-radius: 15px;">
+      <p>If you use PhillNet 1, please provide credit to the original author, Phillip Holland, and review the LICENSE.md for usage guidelines. Your acknowledgement helps foster ethical and responsible AI development.</p>
+    </div>
+  </div>
+  
+  <div class="section">
+    <div class="section-header">
+      <h2 class="section-title">🚀 Final Thoughts</h2>
+    </div>
+    <div class="section-content">
+      <p>PhillNet 1 is not merely a model—it's a dynamic, self-evolving neural organism. From its adaptive MoE routing and self-regulated introspection to its groundbreaking neuroevolution, every aspect is designed for continuous improvement and rich contextual understanding. Join us on this journey as we push the boundaries of what a living AI can achieve.</p>
+    </div>
+  </div>
+</div>