Model developer: Meta
Built with Meta - Llama 3.1 8B Instruct INT4 ONNX model is the AWQ quantized version of the Meta Llama-3.1-8B-Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture for multilingual dialogue use cases. For more information, please check here. The Llama 3.1 8B Instruct INT4 ONNX model is quantized with TensorRT Model Optimizer.
This model is ready for commercial and research use case.
Steps followed to generate this quantized model:
Download Meta Llama-3.1-8B-Instruct model in Pytorch bfloat16 format from HuggingFace.
Convert PyTorch model to ONNX FP16 using onnxruntime-genai model builder.
Quantize Llama-3.1-8B-Instruct ONNX FP16 model to Llama-3.1-8B ONNX INT4 AWQ model using TensorRT Model Optimizer – Windows.
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 the non-NVIDIA Meta-Llama-3.1-Instruct Model Card.
Governing Terms: Use of this model is governed by the NVIDIA Open Model License Agreement. Additional Information: Llama 3.1 Community License Agreement, Built with Llama.
Meta Llama 3.1 Model Card on Hugging face
Meta Llama 3 blogpost
Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
Architecture Type: Transformer
Network Architecture: Llama 3.1
Input Type: Text
Input Format: String
Input Parameters: Sequences (1D)
Other Properties Related to Input: Supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai
Output Type(s): Text
Output Format: String
Output Parameters: Sequence (1D)
Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere and newer GPUs. 6GB or higher GPU video memory are recommended. Higher VRAM may be required for larger context length use cases.
Supported Operating System: Windows
Model Version: v1.0
Refer to Llama 3.1 Model Card for the details.
Link: cnn_dailymail
Data Collection Method by dataset: [Automated]
Labeling Method by dataset: [Unknown]
Link:MMLU
Data Collection Method by dataset: [Unknown]
Labeling Method by dataset: [Not Applicable]
Accuracy Scores: MMLU (5 shots):
With GenAI ORT->DML backend, on a desktop RTX 4090 GPU system.
“Overall accuracy” = 66.1
GPU: RTX 4090, RTX 3090.
Windows 11: 23H2
NVIDIA Graphics driver: R565 or higher
Inference Backend: Onnxruntime-GenAI-DirectML
(Note: Please refer to Readme.txt for the detailed instructions.)
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