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
```

Regarding the issue of inconsistent calculation of tokens

#12

by liguoyu3564 - opened Feb 14, 2025

Discussion

liguoyu3564

Feb 14, 2025

Hello, I am using vllm to deploy the inference service. The usage.prompt_tokens data returned by the calling interface is inconsistent with the token obtained using transformers.AutoTokenizer. The following is the test process:
vllm startup command:

docker run -itd \
  --name deepseek-awq \
  --network host \
  --shm-size=1024m \
  --gpus all \
  -v $(pwd):/app \
  --entrypoint "bash" \
  docker.1ms.run/vllm/vllm-openai:v0.7.2 \
  -c " python3 -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port 8000 --enable_prefix_caching  --max-model-len 65536 --trust-remote-code --tensor-parallel-size 8 --quantization moe_wna16 --gpu-memory-util 0.97 --kv-cache-dtype fp8_e5m2 --calculate-kv-scales --served-model-name deepseek-awq --enable-chunked-prefill --model /app/cognitivecomputations/DeepSeek-R1-awq"


curl --request POST \
  --url http://10.1.30.59:8000/v1/chat/completions \
  --header 'Authorization: Bearer sk-ddddddddddddddd' \
  --header 'Content-Type: application/json' \
  --data '{
    "messages": [
        {
            "role": "user",
            "content": "what is you name"
        }
    ],
    "stream": false,
    // "stream_options":{
    //     "include_usage": true
    // },
    "model": "deepseek-awq",
    "temperature": 0.5,
    "presence_penalty": 0,
    "frequency_penalty": 0,
    "top_p": 1
}'


Response:
    "usage": {
        "prompt_tokens": 7,
        "total_tokens": 120,
        "completion_tokens": 113,
        "prompt_tokens_details": null
    },

python code:

# pip3 install transformers
# python3 deepseek_tokenizer.py
import transformers

chat_tokenizer_dir = "./"

tokenizer = transformers.AutoTokenizer.from_pretrained( 
        chat_tokenizer_dir, trust_remote_code=True
        )

result = tokenizer.encode("what is you name")
print(result)

root@A100-GPU-59:/app/cognitivecomputations/DeepSeek-R1-awq# python3 deepseek_tokenizer.py 
[0, 9602, 344, 440, 2329]

They use the same tokenizer configuration

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