Instructions to use G2RPO-A/Qwen3-1.7B-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use G2RPO-A/Qwen3-1.7B-Math with Transformers:

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

pipe = pipeline("text-generation", model="G2RPO-A/Qwen3-1.7B-Math")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("G2RPO-A/Qwen3-1.7B-Math")
model = AutoModelForCausalLM.from_pretrained("G2RPO-A/Qwen3-1.7B-Math", 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 G2RPO-A/Qwen3-1.7B-Math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "G2RPO-A/Qwen3-1.7B-Math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "G2RPO-A/Qwen3-1.7B-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/G2RPO-A/Qwen3-1.7B-Math

SGLang

How to use G2RPO-A/Qwen3-1.7B-Math 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 "G2RPO-A/Qwen3-1.7B-Math" \
    --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": "G2RPO-A/Qwen3-1.7B-Math",
		"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 "G2RPO-A/Qwen3-1.7B-Math" \
        --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": "G2RPO-A/Qwen3-1.7B-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use G2RPO-A/Qwen3-1.7B-Math with Docker Model Runner:
```
docker model run hf.co/G2RPO-A/Qwen3-1.7B-Math
```

Qwen3-1.7B-Math

This checkpoint is released for mathematical reasoning experiments in the G²RPO-A project. It was fine-tuned from Qwen/Qwen3-1.7B using the mathematical guidance data now available as G2RPO-A/Math-Curriculum-1K.

Model identity

The original repository name and generated model card incorrectly labeled this checkpoint as Qwen3-0.6B. The checkpoint's config.json matches the official Qwen3-1.7B configuration on the key architecture fields: 28 layers, hidden size 2048, intermediate size 6144, 16 attention heads, 8 key/value heads, and vocabulary size 151936. The model owner identifies it as the 1.7B mathematical-task checkpoint. This documentation correction does not modify the model weights or configuration.

Quick start

from transformers import pipeline

generator = pipeline(
    "text-generation",
    model="G2RPO-A/Qwen3-1.7B-Math",
    device="cuda",
)
question = "Solve 2x + 3 = 11. Explain your reasoning and give the value of x."
output = generator(
    [{"role": "user", "content": question}],
    max_new_tokens=2048,
    do_sample=True,
    temperature=0.6,
    top_p=0.95,
    top_k=20,
    return_full_text=False,
)[0]
print(output["generated_text"])

Training and framework versions

The original training-generated card records GRPO training with TRL and the following framework versions:

TRL: 0.15.2
Transformers: 4.52.3
PyTorch: 2.5.1
Datasets: 3.6.0
Tokenizers: 0.21.1

Use Transformers 4.52.3 to match that record; the older 4.49.0 Open-R1 dependency pin does not support Qwen3 out of the box.

See the ACL 2026 paper for the project methodology. This card does not assign the paper's benchmark scores to this checkpoint without a matching evaluation record.