Model Developer: Microsoft
The NVIDIA Phi-3.5-mini-Instruct INT4 ONNX model is the quantized version of the Microsoft Phi-3.5-mini-Instruct model which has 3.8B parameters and is a dense decoder-only Transformer model using the same tokenizer as Phi-3 Mini. It supports 128K context length, therefore the model is capable of several long context tasks including long document/meeting summarization, long document QA, long document information retrieval. For more information, please check here. The NVIDIA Phi-3.5-mini-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 Microsoft Phi-3.5-mini-Instruct model in Pytorch bfloat16 format from HuggingFace.
Convert PyTorch model to ONNX FP16 using onnxruntime-genai model builder.
Quantize Phi-3.5-mini-Instruct ONNX FP16 model to Phi-3.5-mini-Instruct 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 Phi3.5-Mini-Model Card.
Governing Terms: Use of this model is governed by the NVIDIA Open Model License Agreement. Additional Information: MIT License.
Phi3.5-mini-Instruct Model Card
Phi-3.5-mini has 3.8B parameters and is a dense decoder-only Transformer model using the same tokenizer as Phi-3 Mini.
Architecture Type: Transformers
Network Architecture: Phi3
Input Type: Text. It is best suited for prompts using chat format.
Input Format: String
Input Parameters: Sequence (1D)
Other Properties Related to Input: Supports Arabic, Chinese, Czech, Danish, Dutch, English, Finnish, French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian
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(s): Windows
Model Version(s): v1.0
Refer to Phi3.5-mini-Instruct 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” = 65.51
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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