|
--- |
|
model_type: DynamicNeuralNetworkForCausalLM |
|
architectures: |
|
- DynamicNeuralNetworkForCausalLM |
|
config: |
|
adaptation_rate: 0.05 |
|
architectures: |
|
- DynamicNeuralNetworkForCausalLM |
|
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: pytorch_model.bin |
|
model_type: llama |
|
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: conversational |
|
library_name: 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. |
|
|
|
🌐 **Welcome to the CustomModelLoader.py Odyssey!** 🌟 |
|
|
|
Embark on a scholarly quest to unlock the potential of your AI model, "ayjays132/phillnet", with our elegantly crafted script. Designed for the curious minds in AI, this guide is your beacon through the realm of machine learning. Let's dive into the script that's set to revolutionize your AI journey! 🚀 |
|
|
|
### The Script Unveiled: CustomModelLoader.py |
|
This script is your trusty companion in the AI landscape, designed to effortlessly awaken your pre-trained model from its slumber in the Hugging Face Hub. Here's a peek into its core: |
|
|
|
``` |
|
# CustomModelLoader.py |
|
|
|
import torch |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
import logging |
|
|
|
# Initialize 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"Error loading the model: {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"Error loading the tokenizer: {e}") |
|
raise |
|
|
|
if __name__ == "__main__": |
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
model_name = "ayjays132/phillnet" # Your model's home in Hugging Face Hub |
|
try: |
|
tokenizer = load_tokenizer(model_name) |
|
model = load_custom_model(model_name, device) |
|
logger.info("Model and tokenizer are ready for action!") |
|
except Exception as e: |
|
logger.error(f"An unexpected twist occurred: {e}") |
|
``` |
|
|
|
### 🛠 How It Works: The Mechanics |
|
|
|
1. **Setting the Stage**: Our script begins by checking whether to summon the powers of CUDA or settle in the CPU realm. |
|
2. **Summoning the Model & Tokenizer**: It then gracefully calls upon the `AutoModelForCausalLM` and `AutoTokenizer` from the Hugging Face Hub, ensuring your model and tokenizer are at the ready. |
|
3. **Error Handling Like a Pro**: Should any mischiefs arise, our script is well-armed with try-except blocks to catch and log any spells gone awry, keeping you informed every step of the way. |
|
|
|
### 🎓 For the AI Scholars: |
|
|
|
This script isn't just a tool; it's a companion designed to make your AI endeavors more productive and enjoyable. Here's how you can harness its power: |
|
|
|
- **Plug & Play**: Simply place this script in your project, and it's ready to go. No complicated setup required! |
|
- **Error Logs**: Detailed logging ensures you're always in the know, making troubleshooting a breeze. |
|
- **Flexibility**: Designed with customization in mind, feel free to tweak the script to fit the unique needs of your scholarly pursuits. |
|
|
|
### 🌟 Final Words of Wisdom: |
|
|
|
With `CustomModelLoader.py` at your side, you're not just loading a model; you're unlocking a world of possibilities. Whether you're fine-tuning for accuracy or predicting the unknown, your AI journey is about to get a whole lot smoother. So, scholars and AI enthusiasts, let the odyssey begin! 🎩✨ |
|
--- |
