QNetworkGPT2Mini: Reinventing Text Generation with AI 📝🤖

Hyperameters used

Here's a consolidated list of hyperparameters for your QNetworkGPT2 RL model:

input_dim: Input dimension for the RL agent.
output_dim: Output dimension for the RL agent.
hidden_dim: Hidden dimension for the RL agent.
num_episodes: Number of training episodes.
generate_interval: Interval for text generation during training.
load_path: Path to load a pre-trained model.
model_name: GPT-2 model architecture name.
max_new_tokens: Maximum new tokens allowed during text generation.
max_length: Maximum sequence length for input data.
sequence_length: Length of sequences in the dataset.
batch_size: Batch size for training.
learning_rate: Learning rate for optimization.
gamma: Discount factor for rewards.
clip_epsilon: Epsilon value for policy loss clipping.
entropy_beta: Beta value for entropy regularization.
epsilon_start: Initial epsilon for epsilon-greedy exploration.
epsilon_end: Minimum epsilon value.
epsilon_decay: Epsilon decay rate.
heuristic_fn: Heuristic function for action selection.
max_new_tokens: Maximum new tokens allowed during text generation.
save_path: Path to save the trained model.

Researchers can use these hyperparameters to configure and train their QNetworkGPT2 RL models effectively for text generation tasks.

Overview

QNetworkGPT2 is an extraordinary AI model that marries Reinforcement Learning (RL) with the power of the GPT-2 language model to create impressive text generation experiences. 🚀

Capabilities

1. Ultimate Flexibility

Craft RL agents for diverse text generation tasks.
Customize hyperparameters effortlessly.
Harness the brilliance of GPT-2 for text generation magic.

2. Q-Network for Mastery

Unleash the QNetwork class for Q-learning in text generation.
Revel in its multi-layer neural network architecture with residual connections and strategic dropout rates.
Empower your model with heuristic functions for ingenious action selection.

3. PPO Algorithm

Embrace the Proximal Policy Optimization (PPO) algorithm for supreme policy updates.
Sculpt policies with the wisdom of experiences and rewards.

4. Tailored RL Environment

Tailor-make your own RL environment for text generation quests.
Reward the AI with BLEU scores and semantic similarity.
Dance through text generation steps with episode-ending conditions.

5. Replay Buffer and Memory

Store and summon experiences with grace in a replay buffer.
Command a replay memory class to oversee experiences like a pro.

6. Epsilon-Greedy Exploration

The agent employs epsilon-greedy exploration for marvelous discoveries.

7. Target Network for Rock-Solid Stability

Keep target networks in check for unwavering stability during Q-learning escapades.

How It Operates

Birth an RL Agent, fine-tuned to your desires.
Train the agent using PPO magic or embrace Q-learning for epic journeys.
Birth text from input data with the policy network.
Evaluate the text's quality using BLEU and semantic beauty.
Commence your custom RL environment for text generation marvels.

Uniqueness and Epicness

The union of RL and GPT-2 for text generation mastery.
Advanced text tasks unfold gracefully with QNetwork and its heuristic powers.
The limitless canvas to create RL agents for every text challenge.
Rewarding text quality and semantic harmony with AI-calculated rewards.
The blueprint for a customizable and adaptable RL text generation paradise.

Get Started Now

Forge your QNetworkGPT2 with personalized hyperparameters.
Unleash the potential with RL-based training.
Conjure text aligned with your task and dream.
Assess the text with metrics and demands.
Fine-tune and enhance for your text generation quest.

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ayjays132/QNetworkGPT2")

model = AutoModelForCausalLM.from_pretrained("ayjays132/QNetworkGPT2")

Set the EOS token as the padding token

tokenizer.pad_token = tokenizer.eos_token

Initialize a conversation history

conversation_history = []

Start a conversation loop

while True: # Get user input user_input = input("You: ")

# Add user input to the conversation history
conversation_history.append(user_input)

# 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("Bot:", generated_response)

# Add bot's response to the conversation history
conversation_history.append(generated_response)

Explore and Create

QNetworkGPT2 is your ticket to exploring new horizons in text generation. From chatbots and content creation to storytelling and beyond, it's your AI companion for all text adventures. 🌟

Embrace innovation, adaptation, and expansion to conquer your unique text generation challenges. Your text generation revolution starts here! 📚🤖

ayjays132
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