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-124m-hf / README.md

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	---
	language:
	- en
	license: mit
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- finance
	- sec-edgar
	- gpt2
	---

	# 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

	```python
	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