Instructions to use deepseek-ai/DeepSeek-R1-Distill-Qwen-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepseek-ai/DeepSeek-R1-Distill-Qwen-32B with Transformers:

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

pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B")
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-Distill-Qwen-32B")
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-32B")
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
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepseek-ai/DeepSeek-R1-Distill-Qwen-32B 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-Distill-Qwen-32B"
# 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-Distill-Qwen-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

SGLang

How to use deepseek-ai/DeepSeek-R1-Distill-Qwen-32B 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-Distill-Qwen-32B" \
    --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-Distill-Qwen-32B",
		"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-Distill-Qwen-32B" \
        --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-Distill-Qwen-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepseek-ai/DeepSeek-R1-Distill-Qwen-32B with Docker Model Runner:
```
docker model run hf.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
```

Poor performance in the leaderboard?

#17

by L29Ah - opened Jan 24, 2025

Discussion

L29Ah

Jan 24, 2025

Why does the model perform so poorly in the open LLM leaderboard, especially compared to the smaller https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
?

urtuuuu

Jan 27, 2025

Also, don't you guys think it's hallucinating like crazy? I'm actually talking about 14B version, but they are similar. For example ask it "events in 1987". Compare it with gpt-4o-mini, (not even o1 mini). Result= 4o-mini gives events that are really from 1987, while r1-distilled barely gets one or two events correctly...

fblgit

Jan 28, 2025

the 14B has no chat template thats why the math scores >0.0, the others on the leaderboard are with chat_template enabled so the score is affected.
@clefourrier do you think we could get the models evaluated without chat_template in the board or we still on the math eval turbulences?

clefourrier

Jan 28, 2025

cc @alozowski do you have the time to evaluate without chat template?

alozowski

Jan 29, 2025

Hi everyone!

Here are the results without the chat template:

{'Model name': 'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B',
 'Precision': 'torch.bfloat16',
 'Revision': 'd66bcfc2f3fd52799f95943264f32ba15ca0003d',
 'Average': 38.37,
 'IFEval': 40.16,
 'BBH': 46.1,
 'MATH Lvl 5': 46.68,
 'GPQA': 23.15,
 'MUSR': 25.16,
 'MMLU-PRO': 48.98}

The results on the Leaderboard are these:

{'Model name': 'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B',
 'Precision': 'torch.bfloat16',
 'Revision': '4569fd730224ec487752bd4954399c6e18bf3aa6',
 'Average': 20.12,
 'IFEval': 41.86,
 'BBH': 17.15,
 'MATH Lvl 5': 0.0,
 'GPQA': 4.59,
 'MUSR': 16.14,
 'MMLU-PRO': 40.96}

It's true, that usually the chat template affects the scores, notably MATH Lvl 5

clefourrier

Jan 29, 2025

•

edited Jan 29, 2025

I think it would be fair to replace the results on the leaderboard by those (edit) without chat template :)

fblgit

Jan 29, 2025

•

edited Jan 29, 2025

I think it would be fair to replace the results on the leaderboard by those with chat template :)

but the ones with chat_template enabled are providing very low score compared to the tests without chat_template.
.. none of them (with or without template) are actually reflecting the model true performance at all..

clefourrier

Jan 29, 2025

Edited to without, wrote too fast, good catch!

They reflect the performance of the model when evaluated in the exact same setup as the other models on the leaderboard.

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