lzwjava/sec-edgar-gpt

SEC-EDGAR GPT-2 124M

A GPT-2 (124M) language model trained from scratch on SEC EDGAR filings (10-K, 10-Q, 8-K, etc.).

Model Details

Property	Value
Architecture	GPT-2 124M (12 layers, 12 heads, 768 hidden)
Parameters	124,475,904
Context Length	1,024 tokens
Tokenizer	GPT-2 BPE (tiktoken)
Training Tokens	~1.55B (1 epoch)
Training Steps	47,000
Validation Loss	2.28
Training Framework	nanoGPT
Training Hardware	NVIDIA RTX 4070 12GB
Training Time	~8 hours
Bias	No (bias=False)

Training Data

SEC EDGAR filings sourced from the SEC-EDGAR corpus on HuggingFace, covering annual reports (10-K), quarterly reports (10-Q), current reports (8-K), and other filing types. Tokenized with GPT-2 BPE into ~1.55B tokens across 16 shards.

Training Config

• Batch size: 4 × 1024 tokens, gradient accumulation 8 → effective batch 32,768 tokens/step
• Optimizer: GPT-3 style (AdamW, lr=6e-4, warmup=2000, cosine decay to 6e-5)
• No dropout, no weight bias

Usage

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model = GPT2LMHeadModel.from_pretrained("lzwjava/sec-edgar-gpt")
tokenizer = GPT2Tokenizer.from_pretrained("lzwjava/sec-edgar-gpt")

prompt = "UNITED STATES SECURITIES AND EXCHANGE COMMISSION"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(input_ids, max_new_tokens=200, temperature=0.8, do_sample=True)
print(tokenizer.decode(output[0]))

Limitations

• Trained for only 1 epoch — coherent for ~200-500 tokens before repetitive loops
• No instruction tuning or RLHF — raw language model
• 124M parameters is small; don't expect state-of-the-art quality
• GPT-2 tokenizer may not handle all financial notation optimally

Training Code

Trained with nanoGPT. Training config available in the source repo.

Citation

@misc{sec-edgar-gpt-124m,
  author = {Zhiwei Li},
  title = {SEC-EDGAR GPT-2 124M},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/lzwjava/sec-edgar-gpt}
}