DeepSeek-R1-FP4

6.1K
265
license:mit
by
nvidia
Language Model
OTHER
1B params
New
6K downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
3GB+ RAM
Mobile
Laptop
Server
Quick Summary

Description: The NVIDIA DeepSeek R1 FP4 model is the quantized version of the DeepSeek AI's DeepSeek R1 model, which is an auto-regressive language model that u...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by DeepSeek-R1-FP4 with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

The entry point of the program need to be protected for spawning processes.text
from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM

def main():

    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    sampling_params = SamplingParams(max_tokens=32)

    llm = LLM(model="nvidia/DeepSeek-R1-FP4", tensor_parallel_size=8, enable_attention_dp=True)

    outputs = llm.generate(prompts, sampling_params)

    # Print the outputs.
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")


# The entry point of the program need to be protected for spawning processes.
if __name__ == '__main__':
    main()
The entry point of the program need to be protected for spawning processes.text
from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM

def main():

    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    sampling_params = SamplingParams(max_tokens=32)

    llm = LLM(model="nvidia/DeepSeek-R1-FP4", tensor_parallel_size=8, enable_attention_dp=True)

    outputs = llm.generate(prompts, sampling_params)

    # Print the outputs.
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")


# The entry point of the program need to be protected for spawning processes.
if __name__ == '__main__':
    main()

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