Create README.md

README.md

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+---
+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 the `transformers` library starting from v4.48.0:
+
+```sh
+pip install -r requirements.txt
+pip install --pre torch==2.7.0.dev20250110+cu126 --index-url https://download.pytorch.org/whl/nightly/cu126 --upgrade
+```
+
+Here is an example code of using the model:
+
+```python
+from modeling_chronogpt import ChronoGPT
+import tiktoken
+
+device = 'cuda:0'
+max_length = 1792
+
+tokenizer = tiktoken.get_encoding("gpt2")
+model = ChronoGPT.from_pretrained("LinyingLyu/ChronoGPT", 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 460 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])
+