The Model replicates the exact same Decoder only Transformer architecture used in GPT. However this model is trained over 5000 epochs with Parallely processed batches of 328 vectors to predict the next token in the subsequent timestep

Bias, Risks, and Limitations

[The Model is a scaled down version of actual GPT architecture build entirely from scratch. It has only 10.8M params unlike GPT with Billions. The model doesnot incorporate autoregressive processings ]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]
batch_size = 64 # how many independent sequences will we process in parallel?
block_size = 256 # what is the maximum context length for predictions?
max_iters = 5000
eval_interval = 500
learning_rate = 3e-4
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 200
n_embed=384
n_head=6 # therefore every head (head_size)= 384//6 = 64 dim
dropout=0.2

Evaluation

step 0: train loss 4.3585, val loss 4.3557
step 500: train loss 2.0170, val loss 2.0917
step 1000: train loss 1.6125, val loss 1.7842
step 1500: train loss 1.4475, val loss 1.6449
step 2000: train loss 1.3575, val loss 1.5784
step 2500: train loss 1.2872, val loss 1.5347
step 3000: train loss 1.2367, val loss 1.5039
step 3500: train loss 1.1909, val loss 1.4929
step 4000: train loss 1.1529, val loss 1.4847
step 4500: train loss 1.1201, val loss 1.4798

Overview & Purpose

To prevent overfitting on the 1.1M character TinyShakespeare dataset, Dropout (0.2) is injected into:

Attention softmax weights (dropping attention edges).
Multi-Head projection layer outputs.
FeedForward Network layer outputs.

Complete System Architecture Summary

====================================================================================================
Layer / Submodule               Input Shape             Output Shape            Parameters
====================================================================================================
Token Embedding Table           (B, T)                  (B, T, 384)             65 x 384 = 24,960
Positional Embedding Table      (T,)                    (T, 384)                256 x 384 = 98,304
----------------------------------------------------------------------------------------------------
Block 1..6 (x6 Stacked):
  ├── LayerNorm 1               (B, T, 384)             (B, T, 384)             2 x 384 = 768
  ├── Multi-Head Attention:
  │   ├── Key Linear            (B, T, 384)             (B, T, 384)             384 x 384 = 147,456
  │   ├── Query Linear          (B, T, 384)             (B, T, 384)             384 x 384 = 147,456
  │   ├── Value Linear          (B, T, 384)             (B, T, 384)             384 x 384 = 147,456
  │   └── Out Projection        (B, T, 384)             (B, T, 384)             384 x 384 + 384 = 147,840
  ├── LayerNorm 2               (B, T, 384)             (B, T, 384)             2 x 384 = 768
  └── FeedForward Net:
      ├── FC 1 (Expand)         (B, T, 384)             (B, T, 1536)            384 x 1536 + 1536 = 591,360
      └── FC 2 (Contract)       (B, T, 1536)            (B, T, 384)             1536 x 384 + 384 = 590,208
----------------------------------------------------------------------------------------------------
Final LayerNorm                 (B, T, 384)             (B, T, 384)             2 x 384 = 768
Language Model Head             (B, T, 384)             (B, T, 65)              384 x 65 + 65 = 25,025
====================================================================================================
Total Parameters: ~10,788,929 (~10.8 Million)
====================================================================================================

Deepmalya25/Decoder-only-transformer

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Model Details

Model Description

Model Sources [optional]

Uses

Bias, Risks, and Limitations

Recommendations

How to Get Started with the Model

Training Details

Training Data

Training Procedure

Preprocessing [optional]

Training Hyperparameters

Evaluation

Overview & Purpose

Complete System Architecture Summary

Deepmalya25/
Decoder-only-transformer