---
model_type: dynamic_neural_network
architectures:
- DynamicNeuralNetwork
config:
  adaptation_rate: 0.05
  architectures:
  - DynamicNeuralNetwork
  complexity_metric: null
  desired_improvement_rate: 0.02
  ecosystem_dynamics:
    environmental_volatility: 0.1
    resource_pool: 1
  embedding_dim: 768
  growth_improvement_threshold: 0.01
  hidden_dim: 2048
  initial_neuron_count: 5000
  innovative_growth_net:
    adaptation_rate: 0.05
    complexity_metric: null
    initial_capacity: 250000
    input_size: 2048
  input_dimension: 768
  low_stability_threshold: 0.01
  max_complexity: 10000
  max_neurons: 250000
  max_sequence_length: 200
  min_epochs_before_growth: 5
  model_filename: dynamic_network.pth
  model_type: dynamic_neural_network
  num_embeddings: 25000
  pruning_improvement_threshold: 0.005
  some_adaptation_rate: 0.05
  stability_threshold: 0.02
  start_token_index: 2
  transformers_version: 4.34.0
license: apache-2.0
datasets:
- vicgalle/alpaca-gpt4
language:
- en
tags:
- text-generation-inference
metrics:
- accuracy
model: >-
  from transformers import DynamicNeuralNetwork, DynamicNeuralNetworkConfig

  custom_model = DynamicNeuralNetwork.from_pretrained("ayjays132/phillnet",
  config=DynamicNeuralNetworkConfig.from_pretrained("ayjays132/phillnet"))
pipeline_tag: text-generation
library_name: adapter-transformers
---

# 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.

---