Instructions to use apple/SimpleSD-30B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use apple/SimpleSD-30B-instruct with Transformers:

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

pipe = pipeline("text-generation", model="apple/SimpleSD-30B-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("apple/SimpleSD-30B-instruct")
model = AutoModelForCausalLM.from_pretrained("apple/SimpleSD-30B-instruct")
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 apple/SimpleSD-30B-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "apple/SimpleSD-30B-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "apple/SimpleSD-30B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/apple/SimpleSD-30B-instruct

SGLang

How to use apple/SimpleSD-30B-instruct 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 "apple/SimpleSD-30B-instruct" \
    --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": "apple/SimpleSD-30B-instruct",
		"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 "apple/SimpleSD-30B-instruct" \
        --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": "apple/SimpleSD-30B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use apple/SimpleSD-30B-instruct with Docker Model Runner:
```
docker model run hf.co/apple/SimpleSD-30B-instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SimpleSD-30B-instruct

This model is an example of the Simple Self-Distillation (SimpleSD) method that improves code generation by fine-tuning a language model on its own sampled outputs—without rewards, verifiers, teacher models, or reinforcement learning. Please see the paper below for more information. This uses Qwen for initialization.

Self-distillation sampling: temperature=1.6, top_p=0.8, top_k=20
Evaluation sampling: temperature=0.9, top_p=0.8, top_k=20

paper: https://arxiv.org/abs/2604.01193

code: https://github.com/apple/ml-ssd

Notes

These are research checkpoints for reproducibility.
They are not optimized Qwen releases.
They don't represent a broader open-source model strategy.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("apple/SimpleSD-30B-instruct")
tokenizer = AutoTokenizer.from_pretrained("apple/SimpleSD-30B-instruct")

Method

SimpleSD samples solutions from the base model using non-unit temperature and top-k/top-p truncation, then fine-tunes on those samples via standard supervised learning. Despite its simplicity, SimpleSD yields large gains on competitive programming benchmarks, with improvements concentrating on harder problems. The mechanism traces to resolving a precision–exploration conflict: SimpleSD reshapes token distributions in a context-dependent way so that a single global decoding configuration becomes far more effective at evaluation time.

Results

LiveCodeBench (%)

Model	LCBv6 pass@1	LCBv6 pass@5	LCBv5 pass@1	LCBv5 pass@5
Qwen3-30B-A3B-Instruct-2507 (base)	42.4	53.5	45.8	58.7
+ SimpleSD (this model)	55.3 (+12.9)	71.6 (+18.1)	54.3 (+8.5)	70.7 (+12.0)

Paper

Embarrassingly Simple Self-Distillation Improves Code Generation

@misc{zhang2026embarrassinglysimpleselfdistillationimproves,
     title={Embarrassingly Simple Self-Distillation Improves Code Generation},
     author={Ruixiang Zhang and Richard He Bai and Huangjie Zheng and Navdeep Jaitly and Ronan Collobert and Yizhe Zhang},
     year={2026},
     eprint={2604.01193},
     archivePrefix={arXiv},
     primaryClass={cs.CL},
     url={https://arxiv.org/abs/2604.01193},
}