Instructions to use deepseek-ai/DeepSeek-R1-0528-Qwen3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepseek-ai/DeepSeek-R1-0528-Qwen3-8B with Transformers:

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

pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use deepseek-ai/DeepSeek-R1-0528-Qwen3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B

SGLang

How to use deepseek-ai/DeepSeek-R1-0528-Qwen3-8B 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 "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B" \
    --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": "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
		"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 "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B" \
        --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": "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepseek-ai/DeepSeek-R1-0528-Qwen3-8B with Docker Model Runner:
```
docker model run hf.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
```

DeepSeek-R1-Lite

by Dampfinchen - opened May 29, 2025

Discussion

Dampfinchen

May 29, 2025

•

edited May 29, 2025

Hello. Those distills are good and all, but they don't have much to do with R1 as its a completely different architecture. What I would like to see is R1 on the same architecture, just scaled down similar to how V2-Lite was. I think that would be much more compelling and it would also support cutting edge features like MLA which cuts down memory usage during inference a lot.

Doctor-Chad-PhD

May 29, 2025

Yes V2 Lite was the perfect MoE size.

Dampfinchen

May 29, 2025

Yes V2 Lite was the perfect MoE size.

V2 Lite is a little small perhaps. I think the Qwen 3 MoE has almost the perfect size. Maybe with 2B more activated parameters it could be even more capable.

so-anyway

May 29, 2025

Yes please!

DOFOFFICIAL

May 29, 2025

No. That's the meaning of distill. Without changing an arch, just use new data from teacher to train!

djuna

May 29, 2025

We already have deepseek V3 lite at home
https://huggingface.co/moonshotai/Moonlight-16B-A3B-Instruct
Deepseek could continue pretrain and supervised fine-tune

yzlnew

Jun 4, 2025

We already have deepseek V3 lite at home
https://huggingface.co/moonshotai/Moonlight-16B-A3B-Instruct
Deepseek could continue pretrain and supervised fine-tune

No, Moonlight uses a different tokenizer.

djuna

Jun 4, 2025

Moonlight uses a different tokenizer.

What's the problem? Dataset is text isn't it? And the tools like axolotl and other will handle the rest

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