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—without rewards, verifiers, teacher models, or reinforcement learning.
- Base model: 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
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")
License
This model is released under the Apple Machine Learning Research Model License.