deepseek-ai/DeepSeek-R1-0528-Turbo

The NVIDIA DeepSeek-R1-0528-FP4 model is the quantized version of the DeepSeek AI's DeepSeek R1 0528 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA DeepSeek R1 FP4 model is quantized with TensorRT Model Optimizer.

This model is ready for commercial/non-commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (DeepSeek R1) Model Card.

License/Terms of Use:

MIT

Model Architecture:

Architecture Type: Transformers
Network Architecture: DeepSeek R1

Input:

Input Type(s): Text
Input Format(s): String
Input Parameters: 1D (One Dimensional): Sequences
Other Properties Related to Input: DeepSeek recommends adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance: \

• Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
• Avoid adding a system prompt; all instructions should be contained within the user prompt.
• For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."
• When evaluating model performance, it is recommended to conduct multiple tests and average the results.
  

Output:

Output Type(s): Text
Output Format: String
Output Parameters: 1D (One Dimensional): Sequences

Software Integration:

Supported Runtime Engine(s):

• TensorRT-LLM
  

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Blackwell
  

Preferred Operating System(s):

• Linux
  

Model Version(s):

** The model is quantized with nvidia-modelopt v0.31.0

Training Dataset:

** Data Collection Method by dataset: Hybrid: Human, Automated
** Labeling Method by dataset: Hybrid: Human, Automated

Testing Dataset:

** Data Collection Method by dataset: Hybrid: Human, Automated
** Labeling Method by dataset: Hybrid: Human, Automated

Evaluation Dataset:

** Data Collection Method by dataset: Hybrid: Human, Automated
** Labeling Method by dataset: Hybrid: Human, Automated

Calibration Datasets:

• Calibration Dataset: cnn_dailymail
  ** Data collection method: Automated.
  ** Labeling method: Undisclosed.
  

Inference:

Engine: TensorRT-LLM
Test Hardware: B200

Post Training Quantization

This model was obtained by quantizing the weights and activations of DeepSeek R1 to FP4 data type, ready for inference with TensorRT-LLM. Only the weights and activations of the linear operators within transformer blocks are quantized. This optimization reduces the number of bits per parameter from 8 to 4, reducing the disk size and GPU memory requirements by approximately 1.6x.