Instructions to use apple/SimpleSD-30B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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license: apple-amlr
base_model:
- Qwen/Qwen3-30B-A3B-Instruct-2507
tags:
- self-distillation
- code-generation
- ssd
library_name: transformers
---
# SSD-Qwen3-30B-A3B-Instruct
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 using standard supervised learning.
- **Base model:** [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507)
- **Variant:** instruct
- **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
## Method
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.
## 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 |
| **+ SSD (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**
Ruixiang Zhang, Richard He Bai, Huangjie Zheng, Navdeep Jaitly, Ronan Collobert, Yizhe Zhang
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("apple/SSD-Qwen3-30B-A3B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("apple/SSD-Qwen3-30B-A3B-Instruct")
```
## Intended Use
Research on code generation and self-distillation methods.
## License
This model is released under the [Apple Machine Learning Research Model License](https://huggingface.co/apple/SSD-Qwen3-30B-A3B-Instruct/blob/main/LICENSE).
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