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
```

update vllm to 0.8.x and meet some trouble

#34

by HuggingLianWang - opened May 6, 2025

Discussion

HuggingLianWang

May 6, 2025

I'm using the recommend script to run this model and it works fine on vllm 0.7.x
Recently, I update vllm to 0.8.x version and this quant model cannot run using the same script.
The error log shows:

  File "/root/miniconda3/lib/python3.11/site-packages/vllm/model_executor/models/deepseek_v2.py", line 620, in <lambda>
    lambda prefix: DeepseekV2DecoderLayer(
                   ^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.11/site-packages/vllm/model_executor/models/deepseek_v2.py", line 508, in __init__
    self.self_attn = attn_cls(
                     ^^^^^^^^^
  File "/root/miniconda3/lib/python3.11/site-packages/vllm/model_executor/models/deepseek_v2.py", line 440, in __init__
    self.mla_attn = Attention(
                    ^^^^^^^^^^
  File "/root/miniconda3/lib/python3.11/site-packages/vllm/attention/layer.py", line 134, in __init__
    self.impl = impl_cls(num_heads, head_size, scale, num_kv_heads,
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/miniconda3/lib/python3.11/site-packages/vllm/attention/backends/triton_mla.py", line 63, in __init__
    raise NotImplementedError(
NotImplementedError: TritonMLA with FP8 KV cache not yet supported

And my script is like

python -u -m vllm.entrypoints.openai.api_server \
  --host=0.0.0.0 \
  --port=9024 \
  --model=./open_source_models/DeepSeek-R1-awq/ \
  --tokenizer=./open_source_models/DeepSeek-R1-awq/ \
  --served-model-name=DeepSeek-R1-awq \
  --gpu-memory-utilization=0.97 \
  --quantization moe_wna16 \
  --kv-cache-dtype fp8_e5m2 \
  --calculate-kv-scales \
  --enable-reasoning \
  --reasoning-parser deepseek_r1\
  --tensor-parallel-size=8 \
  --max-num-seqs=10 \
  --max_model_len=16384 \
  --max-seq-len-to-capture=16384 \
  --trust-remote-code \
  --max-log-len=200

v2ray

May 6, 2025

MLA doesn't support FP8 yet, don't use it.

HuggingLianWang

May 7, 2025

•

edited May 7, 2025

MLA doesn't support FP8 yet, don't use it.

I removed "kv-cache-dtype" && "calculate-kv-scales". Then it works fine. Thx~

  --kv-cache-dtype fp8_e5m2 \
  --calculate-kv-scales \

But I'm concerned if this will affect performance?

v2ray

May 7, 2025

No, because it doesn't support it in the first place, I don't know what you were doing to get it working on 0.7.x.

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