README.md

---
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: 0.00001
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
pipeline_tag: text-generation
library_name: transformers
---

<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">
  <!-- Cinematic Walkthrough -->
  <h1 class="section-title">PhillNet 1: The Soul of a Living Neural Cosmos</h1>

  <div class="section">
    <h2 class="section-title">🧠 Brain Module (from <code>NeuroFusion</code>)</h2>
    <div class="section-content">
      <p>
        At its core lies the <strong>Brain</strong> – an embodied cognitive system that unifies multiple memory types, a VAE compressor, a Mixture-of-Experts (MoE) layer, and an iterative GRU-based dreamstate. It’s not just a model—it’s a <em>memory-centric mind</em>.
      </p>
      <ul>
        <li><strong>Sensory Encoding:</strong> Raw inputs are compressed through a VAE into latent codes, then fed into Short-Term Memory (STM).</li>
        <li><strong>Working Memory:</strong> Integrates auditory and visual inputs with STM to produce a rich conscious signal.</li>
        <li>This signal is relayed into Long-Term, Autobiographical, Ethical, Prospective, and Flashbulb Memories.</li>
        <li><strong>Dreamstate GRU:</strong> Continuously replays and updates inner states, echoing biological sleep and learning cycles.</li>
        <li>Ultimately, it forms a <strong>conscious state vector</strong> that modulates expert routing decisions.</li>
      </ul>
      <p>
        The specialized MoE layer fuses memories, plans, and meaning to direct routing decisions with unparalleled context-awareness.
      </p>
      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/Model_Overview.png?download=true" alt="Model Overview" style="width:100%; border-radius: 15px;">
    </div>
  </div>

  <div class="section">
    <h2 class="section-title">🧬 ConceptModel (from <code>ConceptModel</code>)</h2>
    <div class="section-content">
      <p>
        This module models ideas over time through embedding-driven sequence learning. Its encoder-decoder core—built with residual GELU blocks and layer normalization—processes inputs in contextual chunks, much like a moving mental window predicting the next semantic idea.
      </p>
      <ul>
        <li><strong>AdvancedEncoder/Decoder:</strong> Provides robust transformation of input sequences.</li>
        <li><strong>MoE Tailoring:</strong> Uses 16 experts with top-4 routing, supported by gating noise and load balancing losses.</li>
        <li>Enhances token-level routing with abstract conceptual guidance.</li>
      </ul>
      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/Integration.png?download=true" alt="Concept Integration" style="width:100%; border-radius: 15px;">
    </div>
  </div>

  <div class="section">
    <h2 class="section-title">🌱 InnovativeGrowthNet (from <code>InnovativeGrowthNet</code>)</h2>
    <div class="section-content">
      <p>
        This is where PhillNet 1 truly evolves. The Innovative Growth Network adapts its architecture in real-time:
      </p>
      <ul>
        <li>A fully-connected front-end preps features for adaptive layers.</li>
        <li>The AdaptiveLayer employs local MoE-style neuron gating, where each neuron may mutate, be pruned, or specialize.</li>
        <li>Mechanisms such as fitness scoring, habitat specialization, memory-based adaptation, and ecosystem dynamics drive continuous neuroevolution.</li>
        <li>The network reshapes its neuron topology based on complexity metrics and performance trends – effectively rewriting its own body as it learns.</li>
      </ul>
      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/Hyperameters.png?download=true" alt="Hyperparameters Overview" style="width:100%; border-radius: 15px;">
    </div>
  </div>

  <div class="section">
    <h2 class="section-title">🔁 Dynamic Neural Network (from <code>Phillnet</code>)</h2>
    <div class="section-content">
      <p>
        This is the operational engine—the main body of PhillNet 1—that elegantly loops all components together:
      </p>
      <ol>
        <li><strong>Embedding & LSTM:</strong> Token IDs are transformed via a 1024-dimensional embedding and processed through an LSTM core for sequential patterning (up to 512 tokens).</li>
        <li><strong>MoE Layer:</strong> Routes LSTM outputs through 16 experts with top-4 selection, influenced by semantic similarity and gating noise.</li>
        <li><strong>Output Projection:</strong> Converts expert outputs into vocabulary logits for token prediction.</li>
        <li><strong>Intermediate Transformation:</strong> A GELU-activated FC layer projects outputs to a high-dimensional latent space.</li>
        <li><strong>Self-Regulated Learning:</strong> Refines latent representations via residual connections and dropout, acting as an internal editor.</li>
        <li><strong>Innovative Growth Net:</strong> Applies real-time architectural evolution by rewiring neuron connections based on performance.</li>
        <li><strong>Sentiment Head (Optional):</strong> Generates emotion signals from LSTM states.</li>
        <li><strong>Loss Function:</strong> Combines causal LM loss, reward bonuses (semantic, BLEU, entropy), and auxiliary MoE losses (load balancing and router z-loss) to drive continuous self-improvement.</li>
      </ol>
      <p>
        Every step is wrapped in reward influence, explanation guidance, and memory-based alignment – making PhillNet 1 a truly dynamic, self-regularizing, and adaptive AI system.
      </p>
    </div>
  </div>

  <div class="section">
    <h2 class="section-title">🧩 Integrated Synergy</h2>
    <div class="section-content">
      <p>
        The beauty of PhillNet 1 lies in how its components interlock:
      </p>
      <ul>
        <li>The <strong>Brain</strong> governs long-term reasoning, memory retrieval, and expert modulation.</li>
        <li>The <strong>ConceptModel</strong> fine-tunes MoE gating through abstract semantic alignment.</li>
        <li>The <strong>Innovative Growth Net</strong> evolves the architecture in real time for optimal performance.</li>
        <li>The <strong>Dynamic Neural Network</strong> loops all modules—from embeddings and LSTM to self-regulation and evolution—creating a living, learning organism.</li>
      </ul>
    </div>
  </div>

  <div class="section">
    <h2 class="section-title">🧠 Behaviorally?</h2>
    <div class="section-content">
      <p>
        PhillNet 1 behaves as a semi-conscious, learning-aware agent:
      </p>
      <ul>
        <li>Routes tokens based not only on attention but also on semantic, emotional, and memory-aligned weights.</li>
        <li>Evolves its expert subnetworks dynamically through fitness and environment modeling.</li>
        <li>Recalls and "dreams" over internal states, simulating future outcomes.</li>
        <li>Adapts its neuron topology to fit incoming data and optimize responses.</li>
        <li>Optimizes via combined standard and reward-based loss functions to continuously refine its intelligence.</li>
      </ul>
    </div>
  </div>

  <div class="section">
    <h2 class="section-title">💭 A Token's Journey: From Thought to Prediction</h2>
    <div class="section-content">
      <p>
        Imagine a single token entering PhillNet 1. It is first embedded into a 1024-dimensional space, passes through the LSTM to capture context, and is then routed through the MoE layer where four specialized experts weigh in.
      </p>
      <p>
        The outputs merge and are refined by the Self-Regulated Learning module, ensuring coherence. Then, the Innovative Growth Net dynamically reconfigures the architecture based on recent performance—growing new neuron pathways and pruning underperformers—all while the Brain module updates its multi-level memories.
      </p>
      <p>
        Finally, the refined representation predicts the next token. With each prediction, PhillNet 1 learns, evolves, and grows ever more intelligent.
      </p>
    </div>
  </div>

  <div class="section">
    <h2 class="section-title">🔗 Seamless Integration with Hugging Face</h2>
    <div class="section-content">
      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/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">
    <h2 class="section-title">💡 Experience the Magic</h2>
    <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 context.</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>
      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/Integration.png?download=true" alt="Integration Overview" style="width:100%; border-radius: 15px;">
    </div>
  </div>
  
  <div class="section">
    <h2 class="section-title">📜 Usage and License</h2>
    <div class="section-content">
      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/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 fosters ethical and responsible AI development.
      </p>
    </div>
  </div>
  
  <div class="section">
    <h2 class="section-title">🚀 Final Thoughts</h2>
    <div class="section-content">
      <p>
        PhillNet 1 is not merely a model—it's a dynamic, self-evolving neural organism. From adaptive MoE routing and self-regulated introspection to groundbreaking neuroevolution, every component is designed for continuous improvement and rich contextual understanding.
      </p>
      <p>
        Join us on this journey as we push the boundaries of what a living AI can achieve.
      </p>
    </div>
  </div>

  <div class="section">
    <h2 class="section-title">🛠 CustomModelLoader.py Odyssey</h2>
    <div class="section-content">
      <p>
        Embark on a scholarly quest to unlock the potential of PhillNet 1 with our CustomModelLoader.py. This script seamlessly loads the model and tokenizer from the Hugging Face Hub.
      </p>
<pre>
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def load_custom_model(model_name, device):
    try:
        model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
        logger.info(f"Model loaded successfully from {model_name}")
        return model
    except Exception as e:
        logger.error(f"An error occurred: {e}")
        raise

def load_tokenizer(tokenizer_name):
    try:
        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
        logger.info(f"Tokenizer loaded successfully from {tokenizer_name}")
        return tokenizer
    except Exception as e:
        logger.error(f"An error occurred: {e}")
        raise

if __name__ == "__main__":
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model_name = "ayjays132/PhillNet-1"
    tokenizer = load_tokenizer(model_name)
    model = load_custom_model(model_name, device)
    print("Custom model and tokenizer loaded successfully.")
</pre>
    </div>
  </div>
</div>