---
language:
- en
license: apache-2.0
tags:
- gpt2
- pytorch
- causal-lm
- text-generation
- fineweb
datasets:
- HuggingFaceFW/fineweb-edu
---

# LiteGPT-Base

This is a **124M parameter** Language Model (GPT-2 Small architecture) pre-trained from scratch on the **FineWeb-Edu** dataset.

It is the base model for [LiteGPT-Instruct](https://huggingface.co/koganrath/LiteGPT-Instruct).

## Model Details

- **Architecture**: GPT-2 Small (12 layers, 12 heads, 768 embedding dim)
- **Parameters**: ~124 Million
- **Context Length**: 1024 tokens
- **Training Data**: 10 Billion tokens from [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) (Sample 10BT).
- **Tokenizer**: GPT-2 (TikToken)

## Usage

This is a **completion model**. It predicts the next tokens based on the input text. It is NOT an instruction-following model (chatbot).

### Python Example

```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer

model = GPT2LMHeadModel.from_pretrained("koganrath/LiteGPT-Base")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

text = "Once upon a time in a digital world,"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Limitations

- **Size**: 124M parameters is small by modern standards.
- **Coherence**: Long-form generation may lose coherence.
- **Knowledge**: Limited to the training data cut-off and scope.

## Authors

Trained by **koganrath** as part of the LiteGPT Project.