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
```

Add instructions to run R1-AWQ on SGLang

#22

by ganler - opened Feb 26, 2025

base: refs/heads/main

←

from: refs/pr/22

Discussion Files changed

+12

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README.md +12 -0

README.md CHANGED Viewed

@@ -13,6 +13,8 @@ AWQ of DeepSeek R1.
 This quant modified some of the model code to fix an overflow issue when using float16.
 To serve using vLLM with 8x 80GB GPUs, use the following command:
 ```sh
 VLLM_WORKER_MULTIPROC_METHOD=spawn python -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port 12345 --max-model-len 65536 --max-num-batched-tokens 65536 --trust-remote-code --tensor-parallel-size 8 --gpu-memory-utilization 0.97 --dtype float16 --served-model-name deepseek-reasoner --model cognitivecomputations/DeepSeek-R1-AWQ
@@ -26,3 +28,13 @@ Inference speed with batch size 1 and short prompt:
 Note:
 - Inference speed will be better than FP8 at low batch size but worse than FP8 at high batch size, this is the nature of low bit quantization.
 - vLLM supports MLA for AWQ now, you can run this model with full context length on just 8x 80GB GPUs.

 This quant modified some of the model code to fix an overflow issue when using float16.
+## Serving with vLLM
 To serve using vLLM with 8x 80GB GPUs, use the following command:
 ```sh
 VLLM_WORKER_MULTIPROC_METHOD=spawn python -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port 12345 --max-model-len 65536 --max-num-batched-tokens 65536 --trust-remote-code --tensor-parallel-size 8 --gpu-memory-utilization 0.97 --dtype float16 --served-model-name deepseek-reasoner --model cognitivecomputations/DeepSeek-R1-AWQ
 Note:
 - Inference speed will be better than FP8 at low batch size but worse than FP8 at high batch size, this is the nature of low bit quantization.
 - vLLM supports MLA for AWQ now, you can run this model with full context length on just 8x 80GB GPUs.
+## Serving with SGLang
+```sh
+python3 -m sglang.launch_server --model cognitivecomputations/DeepSeek-R1-AWQ --tp 8 --trust-remote-code --dtype half
+```
+Note:
+- AWQ does not support BF16, so add the `--dtype half` flag if AWQ is used for quantization.
+- For more information about running DeepSeek-R1 using SGLang, feel free to check out their [documentation](https://docs.sglang.ai/references/deepseek.html).