Update README.md

README.md

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 ---
+model_type: GPT2LMHeadModel
+architectures:
+- GPT2LMHeadModel
+model_filename: pytorch_model.bin
+config:
+  activation_function: gelu_new
+  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
+  n_ctx: 256
+  n_embd: 256
+  n_head: 16
+  n_layer: 24
+  n_positions: 256
+  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: 200
+  vocab_size: 100314
 license: apache-2.0
+datasets:
+- vicgalle/alpaca-gpt4
+language:
+- en
+metrics:
+- bleu
+- accuracy
+library_name: transformers
+pipeline_tag: text-generation
 ---
+
+# QNetworkGPT2Mini: Reinventing Text Generation with AI 📝🤖
+
+![Text Generation](https://static.vecteezy.com/system/resources/previews/023/477/674/non_2x/ai-generative-blue-red-ink-splash-illustration-free-png.png)
+
+---
+## 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
+
+1. Birth an RL Agent, fine-tuned to your desires.
+2. Train the agent using PPO magic or embrace Q-learning for epic journeys.
+3. Birth text from input data with the policy network.
+4. Evaluate the text's quality using BLEU and semantic beauty.
+5. 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
+
+1. Forge your QNetworkGPT2 with personalized hyperparameters.
+2. Unleash the potential with RL-based training.
+3. Conjure text aligned with your task and dream.
+4. Assess the text with metrics and demands.
+5. 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! 📚🤖