Mistral-NeMo is a Large Language Model (LLM) composed of 12B parameters. This model leads accuracy on popular benchmarks across common sense reasoning, coding, math, multilingual and multi-turn chat tasks; it significantly outperforms existing models smaller or similar in size.
The NVIDIA Mistral-Nemo-12B Instruct ONNX INT4 model is quantized with TensorRT Model Optimizer.
Steps followed to generate this quantized model:
Download Mistral-Nemo-12B Instruct model in Pytorch bfloat16 format from HuggingFace.
Convert PyTorch model to ONNX FP16 using onnxruntime-genai model builder.
Quantize Mistral-Nemo-12B Instruct ONNX FP16 model to Mistral-Nemo-12B 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 Non-NVIDIA Mistral-Nemo-12B-Instruct Model Card
Governing Terms: Use of this model is governed by the NVIDIA Open Model License Agreement. Additional Information:Apache License, Version 2.0.
Mistral NeMo 12B Blogpost
Mistral NeMo, a 12B model built in collaboration with NVIDIA. Mistral NeMo offers a large context window of up to 128k tokens. Its reasoning, world knowledge, and coding accuracy are state-of-the-art in its size category. As it relies on standard architecture, Mistral NeMo is easy to use and a drop-in replacement in any system using Mistral 7B.
Architecture Type: Transformer
Network Architecture: Mistral
Input
Input Type: Text
Input Format: String
Input Parameters:1D
Other Properties Related to Input: max_tokens, temperature, top_p, stop, frequency_penalty, presence_penalty, seed
Output
Output Type: Text
Output Format: String
Output Parameters: 1D
Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere and newer GPUs. 6GB or higher VRAM GPUs 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 Mistral-Nemo-12B-Instruct Model Card for the details.
Link:cnn_daily mail
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]
MMLU (5# shots): With GenAI ORT->DML backend, the following accuracy metrics were run on a desktop RTX 4090 GPU system. "overall_accuracy": 66.74
Test configuration:
GPU: RTX 4090.
Windows 11: 23H2
NVIDIA Graphics driver: R565 or higher
We used GenAI ORT->DML backend for inference. The instructions to use this backend are given in readme.txt file available under “Files” tab.
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