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README.md

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+---
+library_name: pytorch
+license: mit
+language:
+- en
+tags:
+- chronologically consistent
+- instruction following
+- modded-nanogpt
+- large language model
+- lookahead-bias-free
+pipeline_tag: text-generation
+inference: false
+---
+
+# ChronoGPT-Instruct
+
+ChronoGPT-Instruct is a family of **chronologically consistent, instruction-following large language models (LLMs)** that eliminate lookahead bias by training exclusively on time-stamped data available **before a fixed knowledge-cutoff date τ**.  
+Each `ChronoGPT-Instruct-τ` extends the `ChronoGPT-τ` base models through supervised instruction fine-tuning while strictly maintaining temporal separation from all post-τ information.
+
+These models provide the research community with a transparent, replicable benchmark for testing **lookahead-bias-free prediction** in economics, finance, and other time-sensitive domains.
+
+---
+
+## 🔍 Model Overview
+
+| Property | Description |
+|:--|:--|
+| **Architecture** | Transformer-decoder |
+| **Parameters** | ≈ 1.55 B |
+| **Layers** | 52 layers |
+| **Embedding dim** | 1,536 |
+| **Context length** | 1,792 tokens |
+| **Tokenizer** | `GPT2Tokenizer` (Hugging Face) |
+| **Training stage** | Pretraining + Instruction Fine-tuning (SFT) |
+| **License** | MIT |
+| **Languages** | English |
+
+---
+
+## 🧠 Training & Data
+
+### Chronological Consistency
+Each model’s corpus satisfies chronologically consistency in both pretraining and instruction-finetuning phases. Texts dated after the model year are excluded, ensuring zero overlap with evaluation data. A GPT-4.1 classifier screens every instruction-response pair.
+
+### Instruction-Finetuning Corpus
+| Stage | Source | # Examples | Avg Length |
+|:--|:--|:--:|:--:|
+| 1 | LLMs-from-Scratch | 1 097 | 102 |
+| 2 | GPT-3 Self-Instruct | 67 136 | 183 |
+| 3 | AllenAI Tulu-3 Mixture | 356 886 | 2 513 |
+
+Only English, non-code entries with pre-2000 content (classifier label = 0 & confidence = 10) are retained.
+
+We release the SFT dataset at https://huggingface.co/datasets/manelalab/ChronoInstruct-SFT.
+
+---
+
+## 🚀 Usage Examples
+
+You can try ChronoGPT-instruct directly in your browser via Google Colab:
+
+<p align="left">
+  <a href="https://colab.research.google.com/github/LinyingLyu/ChronoGPT/blob/main/ChronoGPT_instruct_tutorial.ipynb" target="_blank">
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"/>
+  </a>
+</p>
+
+---
+
+## 👩‍💻 Citation
+
+```
+@article{He_Lv_Manela_Wu_chronogpt_2025,
+  title={Chronologically Consistent Generative AI},
+  author={He, Songrun and Lv, Linying and Manela, Asaf and Wu, Jimmy},
+  journal={Working Paper},
+  year={2025}
+}
+```