AdbhutMOE is a miniature, from-scratch Mixture-of-Experts (MoE) autoregressive language model based on the Mixtral architecture. This model was pre-trained on a sample of the ag_news dataset as part of a learning exercise to demonstrate the end-to-end pipeline for creating a sparse foundation model.

This model is intended for educational purposes only. It showcases how to configure and train an MoE model, which uses a sparse activation pattern to increase parameter count while maintaining a manageable computational cost.

Developed by: rohitnagareddy
Model type: Mixture-of-Experts Causal Language Model
Language: English
License: MIT

How to Use

The model can be easily loaded for text generation using the transformers library pipeline.

python

from transformers import pipeline

# Load the model from the Hugging Face Hub
generator = pipeline('text-generation', model='rohitnagareddy/AdbhutMOE')

# Generate text
prompt = "The latest discovery in space exploration is"
output = generator(
    prompt,
    max_length=50,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    temperature=0.7,
    top_k=50
)

print(output[0]['generated_text'])

Model Architecture

AdbhutMOE is a small-scale MoE model with the following configuration:

Number of layers: 4
Hidden dimension: 256
Number of attention heads: 4
Vocabulary size: 8000
Maximum sequence length: 256 positions
Total Experts per Layer: 8
Activated Experts per Token: 2

This architecture results in a significantly higher parameter count than a dense model of similar computational cost, demonstrating the core benefit of the MoE approach.

Training Details

Training Data

The model was pre-trained on a shuffled sample of the ag_news dataset.

Dataset: ag_news
Sample Size: 10000 articles
Preprocessing: The text of each article was extracted and used for training after filtering out empty examples.

Training Procedure

The model was pre-trained using the Hugging Face Trainer on a single GPU.

Framework: PyTorch
Training Steps: 100
Batch Size: 4
Optimizer: AdamW (default)
Objective: Causal Language Modeling (including the router's auxiliary loss to ensure expert load balancing).

Limitations and Intended Use

This model is a proof-of-concept and is not suitable for any real-world application.

The primary goal of this project was to learn and demonstrate the MoE training pipeline. As a result, it has significant limitations:

Limited Coherence: While more capable than a dense model trained for the same number of steps, the output may still lack long-range coherence due to the limited training data and short training cycle.
Confined Knowledge: The model's knowledge is restricted to the 10000 news articles it was trained on.
Bias: The model will reflect the biases inherent in the ag_news dataset.
No Safety Alignment: This is a raw, pre-trained base model and has not undergone any instruction tuning or RLHF. It should not be used in a public-facing capacity.

The intended use is for studying the configuration and training behavior of Mixture-of-Experts models.