---
library_name: transformers
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2-wikitext2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# gpt2-wikitext2

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an wikitext2 dataset.
It achieves the following results on the evaluation set:
- Loss: 6.3377

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.3991        | 1.0   | 2249 | 6.5169          |
| 6.2508        | 2.0   | 4498 | 6.3377          |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2




# Language Model Training Notebook


- **Causal Language Modeling (CLM)**: Training a model to predict the next token in a sequence. (Current model)
- **Masked Language Modeling (MLM)**: Training a model to predict masked tokens in a sequence. 

## Dataset

I used the [Wikitext 2](https://huggingface.co/datasets/wikitext) dataset as an example in this notebook, but you can easily adapt it to use other datasets from the Hugging Face Hub or your own local data.

## Pre-training

I fine-tuned a GPT-2 model using the steps outlined in this notebook.

- **Model Architecture**: I used the `gpt2` model checkpoint.
- **Dataset**: I used the `wikitext-2-raw-v1` dataset.
- **Training Duration**: I trained for 2 epochs.
- **Key Configurations**:
    - **Learning rate**: `2e-5`
    - **Weight decay**: `0.01`
    - **Batch size**: Determined by `per_device_train_batch_size` in `TrainingArguments` (default is 8). Gradient accumulation steps are not set (default is 1).
    - **Optimizer**: AdamW (default in `Trainer`)
    - **Scheduler**: Linear with warmup (default in `Trainer`)
    - **Checkpointing**: Saved checkpoint every epoch, keeping the last 2.

## GPT-2 Architecture

The GPT-2 model is based on a decoder-only transformer architecture. Key architectural details:

- **Type**: Decoder-only Transformer
- **Layers**: 12 transformer blocks (GPT-2 base)
- **Hidden Size**: 768
- **Attention Heads**: 12
- **Vocabulary Size**: 50,257
- **Max Sequence Length**: 1024 tokens
- **Positional Embeddings**: Learned
- **Activation Function**: GELU
- **Layer Normalization**: Applied before attention and feed-forward layers (pre-LN)
- **Causal Self-Attention**: Each token attends only to previous tokens