Instructions to use youngzhong/SOD-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use youngzhong/SOD-1.7B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("youngzhong/SOD-1.7B")
model = AutoModelForCausalLM.from_pretrained("youngzhong/SOD-1.7B")
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

vLLM

How to use youngzhong/SOD-1.7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/youngzhong/SOD-1.7B

SGLang

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

Docker Model Runner
How to use youngzhong/SOD-1.7B with Docker Model Runner:
```
docker model run hf.co/youngzhong/SOD-1.7B
```

SOD-1.7B / README.md

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metadata

language:
  - en
license: apache-2.0
library_name: transformers
base_model: Qwen/Qwen3-1.7B
tags:
  - agent
  - tool-use
  - distillation
  - math
  - code
  - reasoning
pipeline_tag: text-generation

SOD-1.7B

About

SOD-1.7B is a 1.7B student model distilled from a 4B teacher using SOD (Step-wise On-policy Distillation), a method designed for training small language model agents with tool-integrated reasoning capabilities.

SOD addresses the cascading error propagation problem in on-policy distillation for agentic reasoning by introducing an adaptive step-level weighting mechanism that suppresses distillation loss on drifted steps and restores supervision when the student recovers alignment — all at negligible additional computational cost.

Model Information

Attribute	Value
Base Model	Qwen3-1.7B
Teacher Model	SOD-GRPO_teacher-4B
Training Pipeline	Cold-Start SFT → SOD (Step-wise On-policy Distillation)
Parameters	1.7B

Related Models

Model	Description
SOD-0.6B	SOD-distilled 0.6B student
SOD-1.7B	SOD-distilled 1.7B student (this model)
SOD-GRPO_teacher-4B	GRPO-trained 4B teacher model

Performance

We report average@32 over 5 runs on challenging math, science, and code benchmarks.

1.7B Student Results

Method	AIME 2024	AIME 2025	GPQA-Diamond	LiveCodeBench-v6	Average
Vanilla	9.90	8.96	26.80	22.73	17.10
SFT	26.77	22.40	29.85	24.63	25.91
GRPO	25.63	21.67	33.55	20.70	25.39
OPD	43.86	37.04	31.73	32.45	36.27
OPSD_gt	33.85	24.69	35.02	22.73	29.07
OPSD_hint	34.42	21.43	33.46	23.12	28.11
SOD (This Model)	50.83	41.72	38.72	40.63	42.98

Teacher Model (4B)

Method	AIME 2024	AIME 2025	GPQA-Diamond	LiveCodeBench-v6	Average
GRPO	67.60	60.42	55.19	63.13	61.59

Key Highlights

🏆 Recovers 69.8% of teacher performance with only 1.7B parameters (42.98 vs 61.59)
📈 +18.5% over second-best baseline (OPD) on average
💡 Minimal extra compute: The divergence metric reuses log-probabilities already computed in the forward pass

Citation

@article{zhong2026sod,
      title={SOD: Step-wise On-policy Distillation for Small Language Model Agents}, 
      author={Qiyong Zhong and Mao Zheng and Mingyang Song and Xin Lin and Jie Sun and Houcheng Jiang and Xiang Wang and Junfeng Fang},
      journal={arXiv preprint arXiv:2605.07725},
      year={2026}
}