Instructions to use RoyceLu/Qwen3-0.6B-OpenR1-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RoyceLu/Qwen3-0.6B-OpenR1-SFT with Transformers:

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

pipe = pipeline("text-generation", model="RoyceLu/Qwen3-0.6B-OpenR1-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RoyceLu/Qwen3-0.6B-OpenR1-SFT")
model = AutoModelForCausalLM.from_pretrained("RoyceLu/Qwen3-0.6B-OpenR1-SFT", 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 RoyceLu/Qwen3-0.6B-OpenR1-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RoyceLu/Qwen3-0.6B-OpenR1-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RoyceLu/Qwen3-0.6B-OpenR1-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RoyceLu/Qwen3-0.6B-OpenR1-SFT

SGLang

How to use RoyceLu/Qwen3-0.6B-OpenR1-SFT 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 "RoyceLu/Qwen3-0.6B-OpenR1-SFT" \
    --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": "RoyceLu/Qwen3-0.6B-OpenR1-SFT",
		"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 "RoyceLu/Qwen3-0.6B-OpenR1-SFT" \
        --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": "RoyceLu/Qwen3-0.6B-OpenR1-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RoyceLu/Qwen3-0.6B-OpenR1-SFT with Docker Model Runner:
```
docker model run hf.co/RoyceLu/Qwen3-0.6B-OpenR1-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-0.6B-OpenR1-SFT

Qwen3-0.6B-OpenR1-SFT is a Qwen/Qwen3-0.6B-Base model fine-tuned with the Open-R1 SFT distillation recipe on the open-r1/Mixture-of-Thoughts dataset.

Base Model

Base model: Qwen/Qwen3-0.6B-Base
Architecture: Qwen3ForCausalLM
Max position embeddings: 32768
Intended use: reasoning-oriented text generation

Training Recipe

This run used the Open-R1 supervised distillation recipe derived from recipes/OpenR1-Distill-7B/sft/config_distill.yaml with the base model overridden to Qwen/Qwen3-0.6B-Base and the Hub target set to RoyceLu/Qwen3-0.6B-OpenR1-SFT.

Key settings:

Dataset: open-r1/Mixture-of-Thoughts (all config)
Epochs: 5
Max sequence length: 32768
Learning rate: 4e-5
Per-device train batch size: 2
Gradient accumulation steps: 8
Precision: bf16
Attention implementation: flash_attention_2
Liger kernel: enabled

Data

Training dataset: open-r1/Mixture-of-Thoughts
Training samples seen: 349,317

Hardware And Runtime

Hardware: 1 x node, 8 x NVIDIA H100 80GB
Training runtime: 168,141.7s (1d 22h 42m 22s)
Train steps: 13,650
Final train loss: 0.6757

Evaluation

Benchmarks were evaluated with lighteval + vLLM on single H100 GPUs. For all runs below, both baseline and fine-tuned models used the same inference settings:

max_model_length=8192
temperature=0.6
top_p=0.95
use_chat_template=true

`max_new_tokens=2048`

Benchmark	Qwen/Qwen3-0.6B-Base	Qwen3-0.6B-OpenR1-SFT	Delta (pp)
AIME 2024 (LightEval `pass@1`, 64 samples)	0.05%	0.10%	+0.05 pp
MATH-500 (LightEval `pass@1`, 4 samples)	36.85%	35.40%	-1.45 pp
GPQA Diamond (LightEval `pass@1`, 8 samples)	25.44%	26.96%	+1.52 pp
LiveCodeBench (LightEval `pass@1`, 16 samples)	0.0933%	1.6325%	+1.5392 pp

`max_new_tokens=4096`

Benchmark	Qwen/Qwen3-0.6B-Base	Qwen3-0.6B-OpenR1-SFT	Delta (pp)
AIME 2024 (LightEval `pass@1`, 64 samples)	0.68%	1.41%	+0.73 pp
MATH-500 (LightEval `pass@1`, 4 samples)	36.80%	41.95%	+5.15 pp
GPQA Diamond (LightEval `pass@1`, 8 samples)	24.87%	26.77%	+1.89 pp
LiveCodeBench (LightEval `pass@1`, 16 samples)	0.0466%	0.0231%	-0.0235 pp

Usage

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="RoyceLu/Qwen3-0.6B-OpenR1-SFT",
    device="cuda",
)

messages = [{"role": "user", "content": "Solve x^2 - 5x + 6 = 0."}]
output = pipe(messages, max_new_tokens=512, return_full_text=False)
print(output[0]["generated_text"])

Citation

If you use this model in research, evaluations, or derivative model releases, please cite this model together with Qwen3 and Open-R1.

@misc{lu2026qwen3openr1sft,
  author       = {Royce Lu},
  title        = {Qwen3-0.6B-OpenR1-SFT},
  year         = {2026},
  howpublished = {Hugging Face model repository},
  note         = {Fine-tuned from Qwen/Qwen3-0.6B-Base using the Open-R1 supervised distillation recipe}
}

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Safetensors

Model size

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Tensor type

BF16

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