--- 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 ---

PhillNet 1: The Soul of a Living Neural Cosmos

šŸ§  Brain Module (from NeuroFusion)

At its core lies the Brain ā€“ an embodied cognitive system that unifies multiple memory types, a VAE compressor, a Mixture-of-Experts (MoE) layer, and an iterative GRU-based dreamstate. Itā€™s not just a modelā€”itā€™s a memory-centric mind.

  • Sensory Encoding: Raw inputs are compressed through a VAE into latent codes, then fed into Short-Term Memory (STM).
  • Working Memory: Integrates auditory and visual inputs with STM to produce a rich conscious signal.
  • This signal is relayed into Long-Term, Autobiographical, Ethical, Prospective, and Flashbulb Memories.
  • Dreamstate GRU: Continuously replays and updates inner states, echoing biological sleep and learning cycles.
  • Ultimately, it forms a conscious state vector that modulates expert routing decisions.

The specialized MoE layer fuses memories, plans, and meaning to direct routing decisions with unparalleled context-awareness.

Model Overview

šŸ§¬ ConceptModel (from ConceptModel)

This module models ideas over time through embedding-driven sequence learning. Its encoder-decoder coreā€”built with residual GELU blocks and layer normalizationā€”processes inputs in contextual chunks, much like a moving mental window predicting the next semantic idea.

  • AdvancedEncoder/Decoder: Provides robust transformation of input sequences.
  • MoE Tailoring: Uses 16 experts with top-4 routing, supported by gating noise and load balancing losses.
  • Enhances token-level routing with abstract conceptual guidance.
Concept Integration

šŸŒ± InnovativeGrowthNet (from InnovativeGrowthNet)

This is where PhillNet 1 truly evolves. The Innovative Growth Network adapts its architecture in real-time:

  • A fully-connected front-end preps features for adaptive layers.
  • The AdaptiveLayer employs local MoE-style neuron gating, where each neuron may mutate, be pruned, or specialize.
  • Mechanisms such as fitness scoring, habitat specialization, memory-based adaptation, and ecosystem dynamics drive continuous neuroevolution.
  • The network reshapes its neuron topology based on complexity metrics and performance trends ā€“ effectively rewriting its own body as it learns.
Hyperparameters Overview

šŸ” Dynamic Neural Network (from Phillnet)

This is the operational engineā€”the main body of PhillNet 1ā€”that elegantly loops all components together:

  1. Embedding & LSTM: Token IDs are transformed via a 1024-dimensional embedding and processed through an LSTM core for sequential patterning (up to 512 tokens).
  2. MoE Layer: Routes LSTM outputs through 16 experts with top-4 selection, influenced by semantic similarity and gating noise.
  3. Output Projection: Converts expert outputs into vocabulary logits for token prediction.
  4. Intermediate Transformation: A GELU-activated FC layer projects outputs to a high-dimensional latent space.
  5. Self-Regulated Learning: Refines latent representations via residual connections and dropout, acting as an internal editor.
  6. Innovative Growth Net: Applies real-time architectural evolution by rewiring neuron connections based on performance.
  7. Sentiment Head (Optional): Generates emotion signals from LSTM states.
  8. Loss Function: Combines causal LM loss, reward bonuses (semantic, BLEU, entropy), and auxiliary MoE losses (load balancing and router z-loss) to drive continuous self-improvement.

Every step is wrapped in reward influence, explanation guidance, and memory-based alignment ā€“ making PhillNet 1 a truly dynamic, self-regularizing, and adaptive AI system.

šŸ§© Integrated Synergy

The beauty of PhillNet 1 lies in how its components interlock:

  • The Brain governs long-term reasoning, memory retrieval, and expert modulation.
  • The ConceptModel fine-tunes MoE gating through abstract semantic alignment.
  • The Innovative Growth Net evolves the architecture in real time for optimal performance.
  • The Dynamic Neural Network loops all modulesā€”from embeddings and LSTM to self-regulation and evolutionā€”creating a living, learning organism.

šŸ§  Behaviorally?

PhillNet 1 behaves as a semi-conscious, learning-aware agent:

  • Routes tokens based not only on attention but also on semantic, emotional, and memory-aligned weights.
  • Evolves its expert subnetworks dynamically through fitness and environment modeling.
  • Recalls and "dreams" over internal states, simulating future outcomes.
  • Adapts its neuron topology to fit incoming data and optimize responses.
  • Optimizes via combined standard and reward-based loss functions to continuously refine its intelligence.

šŸ’­ A Token's Journey: From Thought to Prediction

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.

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 performanceā€”growing new neuron pathways and pruning underperformersā€”all while the Brain module updates its multi-level memories.

Finally, the refined representation predicts the next token. With each prediction, PhillNet 1 learns, evolves, and grows ever more intelligent.

šŸ”— Seamless Integration with Hugging Face

PhillNet 1 Model

Load PhillNet 1 easily with the following script: 

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 techniquesā€”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)

šŸ’” Experience the Magic

  • Adaptive Learning: PhillNet 1 continuously refines its internal state via self-regulated learning and neuroevolution.
  • Innovative Growth: Real-time architecture adaptation enables dynamic neuron specialization.
  • Contextual Awareness: Advanced memory modules integrate short-, episodic, and conceptual memories for rich context.

Welcome to a new era of AIā€”where every parameter evolves, every neuron thinks, and every token is a step toward true general intelligence.

Integration Overview

šŸ“œ Usage and License

Usage Example

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.

šŸš€ Final Thoughts

PhillNet 1 is not merely a modelā€”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.

Join us on this journey as we push the boundaries of what a living AI can achieve.

šŸ›  CustomModelLoader.py Odyssey

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. 

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.")