Instructions to use lzwjava/sec-edgar-gpt-124m-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lzwjava/sec-edgar-gpt-124m-hf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lzwjava/sec-edgar-gpt-124m-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lzwjava/sec-edgar-gpt-124m-hf")
model = AutoModelForCausalLM.from_pretrained("lzwjava/sec-edgar-gpt-124m-hf")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lzwjava/sec-edgar-gpt-124m-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lzwjava/sec-edgar-gpt-124m-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lzwjava/sec-edgar-gpt-124m-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lzwjava/sec-edgar-gpt-124m-hf

SGLang

How to use lzwjava/sec-edgar-gpt-124m-hf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lzwjava/sec-edgar-gpt-124m-hf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lzwjava/sec-edgar-gpt-124m-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lzwjava/sec-edgar-gpt-124m-hf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lzwjava/sec-edgar-gpt-124m-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lzwjava/sec-edgar-gpt-124m-hf with Docker Model Runner:
```
docker model run hf.co/lzwjava/sec-edgar-gpt-124m-hf
```

SEC-EDGAR GPT-2 124M

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

Model Details

Parameter	Value
Architecture	GPT-2 (GPT2LMHeadModel)
Parameters	~124M
Layers	12
Hidden size	768
Attention heads	12
Context length	1024
Vocab size	50,257
Precision	float32

Training

Framework: nanoGPT (Karpathy's)
Dataset: SEC-EDGAR filings (financial disclosures, annual/quarterly reports)
Tokenizer: GPT-2 BPE (tiktoken)

Usage

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model = GPT2LMHeadModel.from_pretrained("lzwjava/sec-edgar-gpt-124m-hf")
tokenizer = GPT2Tokenizer.from_pretrained("lzwjava/sec-edgar-gpt-124m-hf")

prompt = "The company reported total revenue of"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.8, top_k=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Use

This model is trained for research and educational purposes — demonstrating nanoGPT training on domain-specific financial text. It is not suitable for production financial analysis or advice.

Limitations

Trained on a subset of SEC filings; may not generalize to all financial domains
No RLHF or instruction tuning — raw language model only
Generated text may contain factual inaccuracies

Downloads last month: 1

Safetensors

Model size

0.2B params

Tensor type

F32