The NVIDIA Llama 3.3 70B Instruct FP4 model is the quantized version of the Meta's Llama 3.3 70B Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Llama 3.3 70B Instruct 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 (Meta-Llama-3.3-70B-Instruct) Model Card.
Input Type(s): Text Input Format(s): String Input Parameters: 1D (One Dimensional): Sequences Other Properties Related to Input: Context length up to 128K
Output:
Output Type(s): Text Output Format: String Output Parameters: 1D (One Dimensional): Sequences Other Properties Related to Output: N/A
This model was obtained by quantizing the weights and activations of Meta-Llama-3.3-70B-Instruct to FP4 data type, ready for inference with TensorRT-LLM. Only the weights and activations of the linear operators within transformers blocks are quantized. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 3.3x.
Usage
Deploy with TensorRT-LLM
To deploy the quantized checkpoint with TensorRT-LLM LLM API, follow the sample codes below:
LLM API sample usage:
from tensorrt_llm import LLM, SamplingParams
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(temperature=0.8, top_p=0.95)
llm = LLM(model="nvidia/Llama-3.3-70B-Instruct-FP4")
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 accuracy benchmark results are presented in the table below:
Precision
MMLU
GSM8K_COT
ARC Challenge
IFEVAL
BF16
83.3
95.3
93.7
92.1
FP4
81.1
92.6
93.3
92.0
Ethical Considerations
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