metadata
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.
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 (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
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.