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

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

File size: 2,491 Bytes

---
license: mit
library_name: transformers
base_model:
- deepseek-ai/DeepSeek-V4-Flash
---


This model is an int4 model with group_size 128 of [deepseek-ai/DeepSeek-V4-Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash) generated by [intel/auto-round](https://github.com/intel/auto-round) with RTN mode. Please follow the license of the original model.



## How to Run Locally

**vLLM and Sglang is not supported currently: https://huggingface.co/Intel/DeepSeek-V4-Flash-W4A16-AutoRound/discussions/1** 

Please refer to the [inference](inference/README.md) folder for detailed instructions on running DeepSeek-V4 locally, including model weight conversion and interactive chat demos.

For local deployment, we recommend setting the sampling parameters to `temperature = 1.0, top_p = 1.0`. For the Think Max reasoning mode, we recommend setting the context window to at least **384K** tokens.


## Generate the Model

This pr is required: [Support model_free WOQ quantization](https://github.com/intel/auto-round/pull/1699)

~~~bash
auto-round  deepseek-ai/DeepSeek-V4-Flash  --model_free --output_dir "./DeepSeek-V4-Flash-W4A16"
~~~



## Ethical Considerations and Limitations

The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.

Therefore, before deploying any applications of the model, developers should perform safety testing.

## Caveats and Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

Here are a couple of useful links to learn more about Intel's AI software:

- [Intel Neural Compressor](https://github.com/intel/neural-compressor)

## Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.

## Cite

@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }

[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)