Instructions to use Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview with Transformers:

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

pipe = pipeline("text-generation", model="Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview")
model = AutoModelForCausalLM.from_pretrained("Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview")
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 Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview

SGLang

How to use Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview 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 "Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview" \
    --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": "Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview",
		"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 "Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview" \
        --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": "Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview with Docker Model Runner:
```
docker model run hf.co/Alibaba-Apsara/DASD-30B-A3B-Thinking-Preview
```

Interesting model - how to control reasoning length?

by cmp-nct - opened Jan 16

Discussion

cmp-nct

Jan 16

The model is interesting, aside of the issues that it believes it's "ChatGPT" I found it to be quite smart, very consistent on longer context range and it's able to maintain attention to tokens over long distances very well.
But how to control it's reasoning length ?

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