Instructions to use QuixiAI/DeepSeek-R1-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuixiAI/DeepSeek-R1-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="QuixiAI/DeepSeek-R1-AWQ", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuixiAI/DeepSeek-R1-AWQ", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("QuixiAI/DeepSeek-R1-AWQ", trust_remote_code=True)
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 QuixiAI/DeepSeek-R1-AWQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuixiAI/DeepSeek-R1-AWQ

SGLang

How to use QuixiAI/DeepSeek-R1-AWQ 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 "QuixiAI/DeepSeek-R1-AWQ" \
    --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": "QuixiAI/DeepSeek-R1-AWQ",
		"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 "QuixiAI/DeepSeek-R1-AWQ" \
        --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": "QuixiAI/DeepSeek-R1-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuixiAI/DeepSeek-R1-AWQ with Docker Model Runner:
```
docker model run hf.co/QuixiAI/DeepSeek-R1-AWQ
```

AMD Instinct MI210 + vllm fail to run this model, any solutions please? Is there any other deepseek-r1-671b models that can run succesfully on AMD Instinct MI210 + vllm? Thanks!

#33

by luciagan - opened Apr 10, 2025

Discussion

luciagan

Apr 10, 2025

Error message：

File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/fused_moe/layer.py", line 372, in init
assert self.quant_method is not None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AssertionError

v2ray

Apr 10, 2025

vLLM version? start up command? OS env?

luciagan

Apr 10, 2025

vLLM version? start up command? OS env?

Hi! @v2ray
Here is the details of vllm version and OS env: https://github.com/vllm-project/vllm/issues/16386

My start up commands are:

Start a docker container:
docker run -it --rm --ipc=host --cap-add=CAP_SYS_ADMIN --device=/dev/kfd --device=/dev/dri/card5 --device=/dev/mem --group-add render --cap-add=SYS_PTRACE --security-opt seccomp=unconfined
--network host
--name dsr1awq
--shm-size 896g
-v "/root/models:/models"
--privileged
-p 6381:6381
-p 1001:1001
-p 2001:2001
-e NCCL_IB_HCA=mlx5
-e NCCL_P2P_DISABLE=1
vllm-dsr1:v1 bash
In the started docker container, run model:
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m vllm.entrypoints.openai.api_server --model /models/DeepSeek-R1-awq --tensor-parallel-size 8 --port 1001 --enforce_eager --distributed-executor-backend mp --pipeline-parallel-size 1 --max-model-len 1024 --dtype float16 --max-num-batched-tokens 1024 --trust-remote-code --enable-prefix-caching

The docker image vllm-dsr1:v1 is the alias of rocm/vllm:rocm6.3.1_instinct_vllm0.7.3_20250311.

Thanks for your help!

v2ray

Apr 10, 2025

vllm0.7.3

Try build from source.

luciagan

Apr 11, 2025

vllm0.7.3

Try build from source.

Hi! Even building from source will still encounter the same assertion error. See https://github.com/vllm-project/vllm/issues/15101

v2ray

Apr 11, 2025

Welp, then I have no idea. I only tested it on CUDA hardware.

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