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---
language:
- en
tags:
- gpt
- mixture-of-experts
- monte-carlo-tree-search
- language-model
license: mit
datasets:
- Finewebedu
metrics:
- perplexity
- accuracy
---
# GPT-MoE-MCTS: GPT with Mixture of Experts and Monte Carlo Tree Search
## Table of Contents
1. [Introduction](#introduction)
2. [Key Features](#key-features)
3. [Model Architecture](#model-architecture)
4. [Installation](#installation)
5. [Usage](#usage)
6. [Training](#training)
7. [Evaluation](#evaluation)
8. [MCTS Decoding](#mcts-decoding)
9. [Contributing](#contributing)
## Introduction
GPT-MoE-MCTS is an advanced language model that combines the power of GPT (Generative Pre-trained Transformer) with Mixture of Experts (MoE) and Monte Carlo Tree Search (MCTS) decoding. This model is designed to provide high-quality text generation with improved efficiency and performance.
## Key Features
- **GPT-based Architecture**: Utilizes the powerful GPT architecture for language modeling.
- **Mixture of Experts**: Incorporates a dynamic routing system to specialize different parts of the network for different inputs.
- **FlashAttention3**: Implements an optimized attention mechanism for improved efficiency.
- **Monte Carlo Tree Search Decoding**: Uses MCTS during inference for higher quality text generation.
- **Hugging Face Compatible**: Easily integrates with the Hugging Face Transformers library.
## Model Architecture
The GPT-MoE-MCTS model consists of the following key components:
1. **Token and Positional Embeddings**: Converts input tokens into embeddings and adds positional information.
2. **Transformer Blocks with MoE**: Multiple layers of transformer blocks, each incorporating:
- FlashAttention3: An optimized attention mechanism.
- Mixture of Experts Layer: A dynamic routing system for specialized processing.
- Feed-Forward Network: Standard MLP for additional processing.
3. **Output Layer**: Final layer normalization and projection to vocabulary logits.
## Installation
To install the GPT-MoE-MCTS model, follow these steps:
```bash
git clone https://github.com/RPasquale/gpt-moe-mcts.git
cd gpt-moe-mcts
pip install -r requirements.txt
```
## Usage
Here's a basic example of how to use the GPT-MoE-MCTS model:
```python
from transformers import GPT2Tokenizer
from modeling_gpt_moe_mcts import GPTMoEMCTSModel
from configuration_gpt_moe_mcts import GPTMoEMCTSConfig
# Initialize configuration and model
config = GPTMoEMCTSConfig()
model = GPTMoEMCTSModel(config)
# Initialize tokenizer (using GPT2Tokenizer as a base)
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# Prepare input
text = "Hello, how are you?"
inputs = tokenizer(text, return_tensors="pt")
# Forward pass
outputs = model(**inputs)
# Get the predicted next token
next_token_logits = outputs.logits[0, -1, :]
next_token = next_token_logits.argmax()
# Decode the predicted token
predicted_text = tokenizer.decode(next_token)
print(f"Input: {text}")
print(f"Predicted next token: {predicted_text}")
```
## Training
To train the GPT-MoE-MCTS model on your own data:
1. Prepare your dataset in the format of tokenized .npy files.
2. Adjust the hyperparameters in the `train_model()` function in `train.py`.
3. Run the training script:
```bash
python train.py
```
The script will automatically save checkpoints and display training progress.
## Evaluation
To evaluate the model's performance:
```python
from eval_utils import evaluate_model
perplexity, accuracy = evaluate_model(model, eval_dataloader)
print(f"Perplexity: {perplexity}, Accuracy: {accuracy}")
```
## MCTS Decoding
The GPT-MoE-MCTS model uses Monte Carlo Tree Search for decoding during inference. To use MCTS decoding:
```python
from mcts_decode import mcts_decode
generated_text = mcts_decode(model, input_text, max_length=50, num_simulations=100)
print(f"Generated text: {generated_text}")
```
## Contributing
We welcome contributions to the GPT-MoE-MCTS project! If you're interested in contributing, please visit our [GitHub repository](https://github.com/RPasquale/gpt-moe-mcts) for more information on how to get involved. You can submit issues, feature requests, or pull requests there.
---
For more detailed information about the model architecture, training process, and advanced usage, please refer to our [documentation](docs/index.md).
If you use GPT-MoE-MCTS in your research, please cite:
```
@misc{GPT-MoE-MCTS,
author = {Robbie Pasquale},
title = {GPT-MoE-MCTS: GPT with Mixture of Experts and Monte Carlo Tree Search},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/RPasquale/gpt-moe-mcts}},
version = {1.0.0},
note = {This project is currently in development.}
}
``` |