Instructions to use Intel/DeepSeek-V4-Flash-W4A16-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Intel/DeepSeek-V4-Flash-W4A16-AutoRound with Transformers:

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

pipe = pipeline("text-generation", model="Intel/DeepSeek-V4-Flash-W4A16-AutoRound")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Intel/DeepSeek-V4-Flash-W4A16-AutoRound")
model = AutoModelForCausalLM.from_pretrained("Intel/DeepSeek-V4-Flash-W4A16-AutoRound")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Intel/DeepSeek-V4-Flash-W4A16-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Intel/DeepSeek-V4-Flash-W4A16-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Intel/DeepSeek-V4-Flash-W4A16-AutoRound",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Intel/DeepSeek-V4-Flash-W4A16-AutoRound

SGLang

How to use Intel/DeepSeek-V4-Flash-W4A16-AutoRound 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 "Intel/DeepSeek-V4-Flash-W4A16-AutoRound" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Intel/DeepSeek-V4-Flash-W4A16-AutoRound",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Intel/DeepSeek-V4-Flash-W4A16-AutoRound" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Intel/DeepSeek-V4-Flash-W4A16-AutoRound",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Intel/DeepSeek-V4-Flash-W4A16-AutoRound with Docker Model Runner:
```
docker model run hf.co/Intel/DeepSeek-V4-Flash-W4A16-AutoRound
```

DeepSeek-V4-Flash-W4A16-AutoRound / inference /README.md

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Inference code for DeepSeek models

First convert huggingface model weight files to the format of this project.

export EXPERTS=256
export MP=8
export CONFIG=config_w4a16.json
python convert_w4a16.py --hf-ckpt-path ${HF_CKPT_PATH} --save-path ${SAVE_PATH} --n-experts ${EXPERTS} --model-parallel ${MP}

Then chat with DeepSeek model at will!

torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --interactive

Or batch inference from file.

torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --input-file ${FILE}

Or multi nodes inference.

torchrun --nnodes ${NODES} --nproc-per-node $((MP / NODES)) --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --input-file ${FILE}