Instructions to use MMOPD/Qwen3-4B-OT3-finance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MMOPD/Qwen3-4B-OT3-finance with Transformers:

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

pipe = pipeline("text-generation", model="MMOPD/Qwen3-4B-OT3-finance")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MMOPD/Qwen3-4B-OT3-finance")
model = AutoModelForCausalLM.from_pretrained("MMOPD/Qwen3-4B-OT3-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 MMOPD/Qwen3-4B-OT3-finance with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MMOPD/Qwen3-4B-OT3-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": "MMOPD/Qwen3-4B-OT3-finance",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MMOPD/Qwen3-4B-OT3-finance

SGLang

How to use MMOPD/Qwen3-4B-OT3-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 "MMOPD/Qwen3-4B-OT3-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": "MMOPD/Qwen3-4B-OT3-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 "MMOPD/Qwen3-4B-OT3-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": "MMOPD/Qwen3-4B-OT3-finance",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MMOPD/Qwen3-4B-OT3-finance with Docker Model Runner:
```
docker model run hf.co/MMOPD/Qwen3-4B-OT3-finance
```

Qwen3-4B-OT3-finance (finance teacher, RL)

Qwen3-4B-OT3-finance is the finance domain teacher of the MMOPD study: MMOPD/Qwen3-4B-OT3-2ep trained with GRPO reinforcement learning on financial-report question answering with numeric answers (FinQA and TAT-QA training questions), using a verifiable numeric-match reward. It raises FinQA from 58.3 to 74.6.

Training


init	`MMOPD/Qwen3-4B-OT3-2ep`
RL	GRPO (NeMo-RL 0.7), 200 steps, 32 prompts x 8 samples per step, LR 3e-6, no KL penalty, 16,384-token rollouts at temperature 1.0
reward	numeric answer match (DocMath-Eval comparator: 0.15% relative tolerance, percent / scale normalisation)
pool	7,565 prompts: TAT-QA train 4,506 + FinQA train 3,059 (numeric-answer questions over report tables and text), decontaminated against the evaluation sets
checkpoint	RL step 200

Evaluation

Domain benchmarks (temperature 1.0, top-p 1.0, long generation budget; accuracy in %):

Model	MedQA	MedXpertQA	PubMedQA	CaseHOLD	FinQA	TAT-QA (EM)
Qwen3-4B-OT3-finance (this)	69.1	–	–	60.8	74.6	–
Qwen3-4B-OT3-2ep (student init)	69.8	13.7	75.2	63.2	58.3	24.4

General benchmarks (Qwen3 thinking preset: temperature 0.6, top-p 0.95, top-k 20; 32,768 max new tokens; AIME = avg@8, LiveCodeBench v6 / IFEval / IFBench = 1 sample; scores in %):

Model	AIME24	AIME25	AIME26	LiveCodeBench v6	IFEval	IFBench
Qwen3-4B-OT3-finance (this)	50.8	55.8	52.1	47.8	34.6	24.3
Qwen3-4B-OT3-2ep (student init)	66.3	56.3	58.3	51.7	51.0	27.7

Notes

Weights are stored in bfloat16 (cast from the fp32 training master weights; this is the precision in which the teacher was evaluated and served). Apache-2.0.
Domain scores are measured at temperature 1.0 because that is the sampling regime in which these models serve as distillation teachers; general benchmarks use the Qwen3 thinking preset.
Part of the MMOPD model family together with MMOPD/Qwen3-4B-OT3-{1ep,2ep}, MMOPD/Qwen3-1.7B-OT3-{1ep,2ep} and the other domain teachers MMOPD/Qwen3-4B-OT3-{medical,law,finance,if}.

How to use

The models keep the Qwen3 chat template and thinking format (<think> ... </think> before the answer). Use enable_thinking=True and sampling (not greedy); the evaluations below used a 32k-token generation budget.

from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "MMOPD/Qwen3-4B-OT3-finance"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="auto", device_map="auto")
messages = [{"role": "user", "content": "Given the income statement excerpt below, what was the percentage change in operating income from 2019 to 2020? ..."}]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
out = model.generate(**tok(text, return_tensors="pt").to(model.device), max_new_tokens=32768,
                     do_sample=True, temperature=0.6, top_p=0.95, top_k=20)
print(tok.decode(out[0], skip_special_tokens=True))

vLLM: vllm serve MMOPD/Qwen3-4B-OT3-finance --max-model-len 40960 (the same sampling settings apply).

Downloads last month: 180

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for MMOPD/Qwen3-4B-OT3-finance

Base model

Qwen/Qwen3-4B-Base

Finetuned

MMOPD/Qwen3-4B-OT3-2ep