README.md

---
library_name: pytorch
license: mit
language:
- en
tags:
- chronologically consistent
- modded-nanogpt
- hellaswag
pipeline_tag: text-generation
inference: false
---
# ChronoGPT

## Model Description

ChronoGPT is a series **high-performance chronologically consistent large language models (LLMs)** designed to eliminate lookahead bias and training leakage while maintaining good language understanding in time-sensitive applications. The model is pretrained on **diverse, high-quality, open-source, and timestamped text** to maintain chronological consistency.

All models in the series achieve **HellaSwag benchmark scores that surpass those of the GPT-2 124M model with the same parameter count.** This approach preserves the integrity of historical analysis and enables more reliable economic and financial modeling.

- **Developed by:** Songrun He, Linying Lv, Asaf Manela, Jimmy Wu
- **Model type:** Transformer-based autoregressive decoder (Modified modded-NanoGPT architecture)
- **Language(s) (NLP):** English
- **License:** MIT License

## Model Sources

- **Paper:** "Chronologically Consistent Large Language Models" (He, Lv, Manela, Wu, 2025)

## How to Get Started with the Model

The model is compatible with following requirements:

```sh
pip install -r requirements.txt
```

Here is an example code of using the model:

```python
from modeling_chronogpt import ChronoGPT
import tiktoken
import torch

device = 'cuda:0'
max_length = 1792

tokenizer = tiktoken.get_encoding("gpt2")
model = ChronoGPT.from_pretrained("manelalab/chrono-gpt-v1-19991231", trust_remote_code=True).to(device)

text = "Obviously, the time continuum has been disrupted, creating a new temporal event sequence resulting in this alternate reality. -- Dr. Brown, Back to the Future Part II"

inputs = torch.tensor(tokenizer.encode(text))[:max_length].reshape(1,-1).to(device)
logits, emb = model(inputs)
```

## Training Details

### Training Data

- **Pretraining corpus:** Our initial model chrono-gpt-v1-19991231 is pretrained on 21 billion tokens of pre-2000, diverse, high-quality, and open-source text data to ensure no leakage of data afterwards.
- **Incremental updates:** Yearly updates from 2000 to 2024 with an additional 65 billion tokens of timestamped text.

### Training Procedure

- **Architecture:** modded NanoGPT-based model with the Muon optimizer, Skip connections, rotary embeddings and flex attention.
- **Objective:** Autoregressive text generation.

## Evaluation

### Testing Data, Factors & Metrics

- **Language understanding:** Evaluated on **HellaSwag benchmark** tasks.
- **Financial forecasting:** Evaluated using **return prediction task** based on Dow Jones Newswire data.
- **Comparison models:** ChronoGPT was benchmarked against **BERT, FinBERT, StoriesLM-v1-1963, and Llama 3.1**.

### Results

- **HellaSwag Score:** chrono-gpt-v1-19991231 and chrono-gpt-v1-20241231 achieved HellaSwag score of 0.295 and 0.324 respectively, outperforming GPT-2 (0.294).
- **Stock return predictions:** During the sample from 2008-01 to 2023-07, chrono-gpt-v1-realtime achieves a long-short portfolio **Sharpe ratio of 4.50**, outperforming BERT, FinBERT, and StoriesLM-v1-1963, and comparable to **Llama 3.1 8B (4.90)**.


## Citation

```
@article{He2025ChronoBERT,
  title={Chronologically Consistent Large Language Models},
  author={He, Songrun and Lv, Linying and Manela, Asaf and Wu, Jimmy},
  journal={Working Paper},
  year={2025}
}
```

## Model Card Authors

- Songrun He (Washington University in St. Louis, [email protected])
- Linying Lv (Washington University in St. Louis, [email protected])
- Asaf Manela (Washington University in St. Louis, [email protected])
- Jimmy Wu (Washington University in St. Louis, [email protected])