Text Generation
Transformers
Safetensors
qwen3
self-distillation
code-generation
conversational
text-generation-inference
Instructions to use apple/SimpleSD-4B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/SimpleSD-4B-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="apple/SimpleSD-4B-instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("apple/SimpleSD-4B-instruct") model = AutoModelForCausalLM.from_pretrained("apple/SimpleSD-4B-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-4B-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "apple/SimpleSD-4B-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-4B-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/apple/SimpleSD-4B-instruct
- SGLang
How to use apple/SimpleSD-4B-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-4B-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-4B-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-4B-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-4B-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use apple/SimpleSD-4B-instruct with Docker Model Runner:
docker model run hf.co/apple/SimpleSD-4B-instruct
Update README
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library_name: transformers
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#
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This model was produced using **Simple Self-Distillation (SSD)**, a method that improves code generation by fine-tuning a language model on its own sampled outputs—without rewards, verifiers, teacher models, or reinforcement learning.
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- **Base model:** [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
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- **Variant:** instruct
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- **Self-distillation sampling:** temperature=1.6, top_p=0.8, top_k=20
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- **Evaluation sampling:** temperature=1.1, top_p=0.8, top_k=20
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- They are not optimized Qwen releases.
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- They don't represent a broader open-source model strategy.
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## Method
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SSD 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, SSD yields large gains on competitive programming benchmarks, with improvements concentrating on harder problems. The mechanism traces to resolving a *precision–exploration conflict*: SSD reshapes token distributions in a context-dependent way so that a single global decoding configuration becomes far more effective at evaluation time.
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## Paper
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**Embarrassingly Simple Self-Distillation Improves Code Generation**
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```
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## License
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This model is released under the [Apple Machine Learning Research Model License](https://huggingface.co/apple/
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library_name: transformers
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---
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# SimpleSD-4B-instruct
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This model was produced using **Simple Self-Distillation (SSD)**, a method that improves code generation by fine-tuning a language model on its own sampled outputs—without rewards, verifiers, teacher models, or reinforcement learning.
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- **Self-distillation sampling:** temperature=1.6, top_p=0.8, top_k=20
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- **Evaluation sampling:** temperature=1.1, top_p=0.8, top_k=20
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- They are not optimized Qwen releases.
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- They don't represent a broader open-source model strategy.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("apple/SimpleSD-4B-instruct")
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tokenizer = AutoTokenizer.from_pretrained("apple/SimpleSD-4B-instruct")
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```
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## Method
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SSD 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, SSD yields large gains on competitive programming benchmarks, with improvements concentrating on harder problems. The mechanism traces to resolving a *precision–exploration conflict*: SSD reshapes token distributions in a context-dependent way so that a single global decoding configuration becomes far more effective at evaluation time.
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## Paper
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[**Embarrassingly Simple Self-Distillation Improves Code Generation**](https://arxiv.org/abs/2604.01193)
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```bibtex
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@misc{zhang2026embarrassinglysimpleselfdistillationimproves,
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title={Embarrassingly Simple Self-Distillation Improves Code Generation},
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author={Ruixiang Zhang and Richard He Bai and Huangjie Zheng and Navdeep Jaitly and Ronan Collobert and Yizhe Zhang},
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year={2026},
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eprint={2604.01193},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2604.01193},
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}
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```
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## License
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This model is released under the [Apple Machine Learning Research Model License](https://huggingface.co/apple/SimpleSD-4B-instruct/blob/main/LICENSE).
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