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Intel
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Model Details
Model Provider
Intel
Model Tree
Base
deepseek-ai/DeepSeek-V4-Flash
Quantized
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
vLLM is supported here: https://github.com/vllm-project/vllm/pull/45645
Please refer to the inference 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.
bash
auto-round deepseek-ai/DeepSeek-V4-Flash \--model_free \--scheme W4A16 \--ignore_layers compressor,indexer.weights_proj \--layer_config "{'wo_a':{bits:16}}" \--output_dir "./DeepSeek-V4-Flash-W4A16"
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.
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:
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.
@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} }