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
```

richardbaihe commited on Mar 20

Commit

0b316b9

verified ·

1 Parent(s): 3adc3ba

update model card and license

Browse files

Files changed (1) hide show

README.md +5 -1

README.md CHANGED Viewed

@@ -11,7 +11,7 @@ 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](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507)
 - **Variant:** instruct
@@ -46,6 +46,10 @@ 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](https://huggingface.co/apple/SSD-Qwen3-30B-A3B-Instruct/blob/main/LICENSE).

 # 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
 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).