Instructions to use lllyx/Qwen3-1.7B-Base-OPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lllyx/Qwen3-1.7B-Base-OPD with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lllyx/Qwen3-1.7B-Base-OPD")
model = AutoModelForCausalLM.from_pretrained("lllyx/Qwen3-1.7B-Base-OPD")
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 lllyx/Qwen3-1.7B-Base-OPD with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/lllyx/Qwen3-1.7B-Base-OPD

SGLang

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

Docker Model Runner
How to use lllyx/Qwen3-1.7B-Base-OPD with Docker Model Runner:
```
docker model run hf.co/lllyx/Qwen3-1.7B-Base-OPD
```

Qwen3-1.7B-Base-OPD

Qwen3-1.7B-Base-OPD is an on-policy distillation (OPD) checkpoint initialized from Qwen3-1.7B-Base. It is distilled from the teacher model Qwen3-4B-Base-GRPO using the DAPO-Math-17k dataset, and is intended for mathematical reasoning and problem-solving.

This model is associated with the paper:
Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe
Paper link: https://arxiv.org/abs/2604.13016

Model Description

This model is obtained by applying on-policy distillation (OPD) to Qwen3-1.7B-Base, with Qwen3-4B-Base-GRPO serving as the teacher model. The OPD training uses DAPO math prompts/data and is designed to transfer the teacher's math-focused reasoning behavior into a smaller 1.7B-parameter student model.

Key characteristics

Student/base model: Qwen3-1.7B-Base
Teacher model: lllyx/Qwen3-4B-Base-GRPO
Training data: DAPO-Math-17k
Training stage: On-Policy Distillation (OPD)
Training framework: verl
Rollout engine: vLLM
Primary domain: Mathematical reasoning
Model architecture: Qwen3ForCausalLM
Precision: bfloat16
Context length: 32768 tokens

Training Details

Training configuration

Base checkpoint: Qwen/Qwen3-1.7B-Base
Teacher checkpoint: lllyx/Qwen3-4B-Base-GRPO
Training framework: verl
Training method: on-policy distillation with GRPO-style rollouts
Distillation loss mode: k1
Policy-gradient term: enabled
Training dataset: DAPO-Math-17k/DAPO-Math.parquet
Primary task domain: math reasoning
Chat template thinking mode: disabled (enable_thinking=False)
Model type: qwen3

Rollout and optimization

Rollout engine: vLLM
Responses per prompt: 4
Prompt length: 1024
Response length: 7168
Max rollout model length: 8193
Train batch size: 64
PPO mini-batch size: 16
PPO micro-batch size per GPU: 1
Max PPO token length per GPU: 8192
Actor learning rate: 1e-6
Total epochs: 1
Save frequency: every 20 steps

Runtime setup

Distributed backend: Ray
Number of nodes: 1
GPUs per node: 4
Teacher world size: 4
Rollout tensor parallel size: 1
Teacher tensor parallel size: 1
Actor training: FSDP with parameter and optimizer offload
Gradient checkpointing: enabled
Padding removal: enabled
Torch compile for actor: enabled
Reward function: rule-based math reward from verl/recipe/r1_ascend/deepscaler.py::compute_score

Dataset

Training data: BytedTsinghua-SIA/DAPO-Math-17k
Teacher rollout/source model: lllyx/Qwen3-4B-Base-GRPO
Student initialization: Qwen/Qwen3-1.7B-Base

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "lllyx/Qwen3-1.7B-Base-OPD"

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

Citation

If you use this model, please consider citing the related paper:

@article{li2026rethinking,
  title={Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe},
  author={Li, Yaxuan and Zuo, Yuxin and He, Bingxiang and Zhang, Jinqian and Xiao, Chaojun and Qian, Cheng and Yu, Tianyu and Gao, Huan-ang and Yang, Wenkai and Liu, Zhiyuan and Ding, Ning},
  journal={arXiv preprint arXiv:2604.13016},
  year={2026}
}