--- activation_function: gelu_new architectures: - GPT2LMHeadModel attn_pdrop: 0.1 bos_token_id: 50256 embd_pdrop: 0.1 eos_token_id: 50256 initializer_range: 0.02 layer_norm_epsilon: 1e-05 model_type: gpt2 n_ctx: 2048 n_embd: 2048 n_head: 16 n_layer: 24 n_positions: 2048 n_special: 0 predict_special_tokens: true resid_pdrop: 0.1 summary_activation: null summary_first_dropout: 0.1 summary_proj_to_labels: true summary_type: cls_index summary_use_proj: true task_specific_params: text-generation: do_sample: true max_length: 2048 transformers_version: 4.34.0 language: - en tags: - text-generation-inference metrics: - accuracy pipeline_tag: text-generation library_name: transformers datasets: - vicgalle/alpaca-gpt4 license: apache-2.0 custom_params: adaptation_rate: 0.05 complexity_metric: null desired_improvement_rate: 0.02 ecosystem_dynamics: environmental_volatility: 0.1 resource_pool: 1 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: 2048 low_stability_threshold: 0.01 max_complexity: 50000 max_neurons: 250000 max_sequence_length: 2048 min_epochs_before_growth: 5 model_filename: pytorch_model.bin num_embeddings: 100000 pruning_improvement_threshold: 0.005 some_adaptation_rate: 0.05 stability_threshold: 0.02 start_token_index: 2 max_input_length: 2048 max_total_tokens: 2051 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: 2048 max_waiting_tokens: 200 --- --- ## Phillnet Large 🚀 ![Text Generation](https://huggingface.co/ayjays132/phillnet/resolve/main/Phillnet.png?download=true) 🚀 Welcome to **Phillnet11b** 🌟 Dive into the depths of AI with Phillnet, your 11 Billion-parameter titan! This colossal model is not just numbers; it's a labyrinth of knowledge, ready to unlock new realms of possibilities. 🧠💡 From text generation to complex problem-solving, Phillnet11b is your gateway to unparalleled AI power. Get ready to embark on a journey where every parameter is a pathway to innovation. 🌍✨ Let's revolutionize the world with **Phillnet5b** - where every bit counts and every neuron fires towards the future! 🚀🔮 --- ## Usage and License ![Text Generation](https://huggingface.co/ayjays132/phillnet/resolve/main/usage.png?download=true) If you intend to use this AI model, I kindly request that you provide appropriate credit to the original author (Phillip Holland) as a gesture of acknowledgment for his effort in developing and sharing this model. **Please adhere to the following guidelines:** 1. **Give Credit:** When you use this AI model, include a visible and prominent credit to the original author (Phillip Holland) in your project, research paper, or application. You can use the following format: 2. **Review the License:** Carefully read and comply with the terms of the accompanying license (LISCENSE.md). The license outlines the permissions and restrictions for using this AI model. Make sure you understand and adhere to its provisions. By using this AI model, you acknowledge that you have read and agreed to the terms and conditions set forth in the license. Thank you for your understanding and cooperation. Together, we can foster responsible and ethical AI development and usage. --- ## Model Overview ![Text Generation](https://huggingface.co/ayjays132/phillnet/resolve/main/Model_Overview.png?download=true) 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:** 2048 - **Hidden Dimension:** 2048 - **Initial Neuron Count:** 5000 - **Input Dimension:** 2048 - **Max Neurons:** 250000 - **Max Sequence Length:** 2048 - **Num Embeddings:** 100000 - **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 --- ## Hyperparameters Overview ![Text Generation](https://huggingface.co/ayjays132/phillnet/resolve/main/Hyperameters.png?download=true) Here's a concise overview of the key hyperparameters used for training the model: **Training Parameters** - `max_neurons`: 250,000 - `epochs`: 50 - `clip`: 5 - `patience`: 7 - `adaptation_rate`: 0.05 - `sequence_length`: 2048 - `max_sequence_length`: 2048 - `weight_decay`: 0.005 - `num_embeddings`: 100,000 - `embedding_dim`: 2048 - `hidden_dim`: 2048 - `learning_rate`: 1e-5 - `some_intermediate_size`: 2048 **Additional Parameters** - `input_dimension`: 2048 - `initial_neuron_count`: 5000 - `some_adaptation_rate`: 0.05 - `complexity_metric`: None **New Parameters** - `growth_improvement_threshold`: 0.01 - `pruning_improvement_threshold`: 0.005 - `stability_threshold`: 0.02 - `max_complexity`: 10,000 - `low_stability_threshold`: 0.01 - `min_epochs_before_growth`: 5 - `desired_improvement_rate`: 0.02 --- ## Seamless Integration with Hugging Face 🤗 ![Text Generation](https://huggingface.co/ayjays132/phillnet/resolve/main/Integration.png?download=true) from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ayjays132/PhillnetLarge") tokenizer.add_special_tokens({'pad_token': '[PAD]'}) model = AutoModelForCausalLM.from_pretrained("ayjays132/PhillnetLarge") # 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 # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) def load_custom_model(model_name, device): try: # Load the model directly from Hugging Face Hub 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 while loading the model: {e}") raise def load_tokenizer(tokenizer_name): try: # Load the tokenizer 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 while loading the tokenizer: {e}") raise def inspect_model_layers(model): logger.info("Inspecting model layers and weights...") for name, param in model.named_parameters(): logger.debug(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]}...\n") if __name__ == "__main__": # Define device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") logger.info(f"Using {'CUDA' if device.type == 'cuda' else 'CPU'}") # Model name or path in Hugging Face Hub model_name = "ayjays132/PhillnetLarge" tokenizer_name = model_name # Assuming tokenizer is at the same path try: # Load the tokenizer and model tokenizer = load_tokenizer(tokenizer_name) model = load_custom_model(model_name, device) # Inspect the model layers and weights inspect_model_layers(model) # Perform a simple test to verify model weights are loaded correctly (Optional) input_ids = tokenizer.encode("Hello, world!", return_tensors="pt").to(device) with torch.no_grad(): outputs = model(input_ids) logger.info("Model test run completed successfully.") print("Custom model and tokenizer loaded successfully.") except Exception as e: logger.error(f"An error occurred: {e}") ``` ### 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! 🎩✨ ### 🛠 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. ---