Instructions to use ctgt-inc/gpt-oss-20b-finance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ctgt-inc/gpt-oss-20b-finance with Transformers:

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

pipe = pipeline("text-generation", model="ctgt-inc/gpt-oss-20b-finance")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ctgt-inc/gpt-oss-20b-finance")
model = AutoModelForCausalLM.from_pretrained("ctgt-inc/gpt-oss-20b-finance", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ctgt-inc/gpt-oss-20b-finance with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ctgt-inc/gpt-oss-20b-finance"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ctgt-inc/gpt-oss-20b-finance",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ctgt-inc/gpt-oss-20b-finance

SGLang

How to use ctgt-inc/gpt-oss-20b-finance 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 "ctgt-inc/gpt-oss-20b-finance" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ctgt-inc/gpt-oss-20b-finance",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ctgt-inc/gpt-oss-20b-finance" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ctgt-inc/gpt-oss-20b-finance",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ctgt-inc/gpt-oss-20b-finance with Docker Model Runner:
```
docker model run hf.co/ctgt-inc/gpt-oss-20b-finance
```

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gpt-oss-20b-finance

ctgt-inc/gpt-oss-20b-finance is a specialized financial reasoning model fine-tuned using self-distillation from gpt-oss-20b. It is explicitly trained to navigate complex, multi-step financial problems where real-world assumptions are ambiguous or require explicitly stated formulas. It can be served on one H100/A100 GPU.

Quickstart

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ctgt-inc/gpt-oss-20b-finance"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = """<|system|>
You are a financial reasoning expert. Break down the following calculation, explicitly state your underlying assumptions, and show all intermediate steps.

<|user|>
A company has a Net Income of $10M, Dividends paid of $2M, and total equity of $50M at the start of the period. Calculate the Sustainable Growth Rate (SGR) under standard assumptions.

<|assistant|>"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=8192, temperature=0.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training & Distillation Details

This model was created via self-distillation, leveraging a synthetic dataset of multi-step reasoning traces based on financial formulas and concepts derived from Investopedia.

Parameter	Detail
Base Model	`gpt-oss-20b`
Teacher Model	Self-distillation (`gpt-oss-20b`)
Dataset Size	220 high-quality synthetic reasoning traces
Data Focus	Multi-step financial calculations with ambiguous/explicit assumptions
Data Source Grounding	Investopedia financial formulas & methodologies
Hardware	2x NVIDIA H100 GPUs
Training Time	~6 Hours

Synthetic Dataset & Task Formulation

Rather than relying on simple question-answering, our synthetic dataset targets complex, multi-step financial reasoning. Prompts are specifically designed around edge cases and scenarios requiring explicit parameter interpretation, such as:

Working capital adjustments
Terminal value growth rates
Dynamic or varying discount rates

Training Pipeline

We fine-tune using Expert LoRA paired with Hint-SD (Hint-based Self-Distillation):

Reasoning Trace Generation: The student model generates a full step-by-step reasoning trace for a given problem.
Teacher Localization & Hint Generation: For incorrect traces, an oracle teacher model (with access to the ground-truth answer) identifies the precise step where the error occurred and writes a targeted corrective hint.
Localized Step Alignment: We optimize a reverse KL divergence loss between the student model output with the hint and without the hint, calculated specifically over the erroneous reasoning step.

Evaluation Results

Evaluated on the FinanceReasoning evaluation suite:

Metric	Score
Average Accuracy	74.79%
Average Execution Rate	93.28%
Total Tokens Generated	486,130

Accuracy: Percentage of correct final numerical and logical reasoning outputs on test cases.
Execution Rate: Rate at which the generated reasoning path successfully outputs an answer without exceeding the token budget.

Intended Use & Limitations

Direct Use

Step-by-step financial formula calculations (FCF, WACC, DCF valuation models, ratios).
Reasoning through ambiguous corporate finance problems by explicitly documenting assumptions.
Explaining complex financial concepts and mechanics.

Limitations & Financial Disclaimer

⚠️ Notice: This model is intended for research, educational, and computational assistance purposes only. It does NOT constitute professional financial, investment, or tax advice. Always independently verify calculations and assumptions before making financial decisions.

License & Acknowledgments

This model is licensed under the Apache 2.0 License.

Special thanks and credit to the creators and open-source contributors of gpt-oss-20b, which served as the base model.

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Safetensors

Model size

21B params

Tensor type

BF16

ctgt-inc
/

gpt-oss-20b-finance