konradhugging/qwen2-onnx-quantized
By konradhugging · ↓ 3 · ♥ 0 · Updated 2024-08-31
What's in this repo
This page lists the files in konradhugging/qwen2-onnx-quantized, including decoder_model_merged.onnx, decoder_model_merged_quantized.onnx and more.
- 4 ONNX models (.onnx files)
- 14 files, including 7 .json, 4 .onnx, 1 .md, 1 .txt
- Last updated 2024-08-31
ONNX files
| File | Size | Download |
|---|---|---|
| decoder_model_merged.onnx | — | Download |
| decoder_model_merged_quantized.onnx | — | Download |
| onnx/decoder_model_merged.onnx | — | Download |
| onnx/decoder_model_merged_quantized.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
Quantized Neural Network Model: Qwen/Qwen2-0.5B-Instruct
Model Details
Model Description
This model is a quantized version of the Qwen/Qwen2-0.5B-Instruct neural network designed to reduce computational and memory requirements while maintaining accuracy. Quantization involves converting the weights and activations of the model from floating-point to lower precision, such as 8-bit integers.
Usage
Intended Use
The quantized model is intended for deployment in resource-constrained environments such as mobile devices, embedded systems, and edge computing scenarios. It is suitable for tasks such as text generation and natural language processing.
Limitations
- The model may exhibit reduced accuracy on certain tasks compared to the original floating-point model.
- Quantization may introduce numerical instability in some cases.
Ethical Considerations
Users should be aware of potential biases in the training data that could affect the model's predictions. It is important to evaluate the model's performance on diverse datasets to ensure fairness and mitigate any unintended biases.
Model info
Created: 2024-08-21
Updated: 2024-08-31
View on Hugging FaceFiles (14)
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