license: mit
pipeline_tag: text-generation
tags:
- ONNX
- DML
- ONNXRuntime
- phi3
- nlp
- conversational
- custom_code
inference: false
Model Summary
Phi-3.5-mini ONNX Conversion The Phi-3.5-mini model has been converted to ONNX format. This conversion allows for:
Improved compatibility across different platforms and frameworks Potential performance optimizations through ONNX Runtime Easier deployment in production environments
Users can now access the ONNX version of Phi-3.5-mini-instruct for their applications. The core capabilities and performance of the model remain unchanged. For usage instructions and more details, please refer to the updated documentation.
https://github.com/microsoft/onnxruntime/tree/main/csharp
Phi-3.5: [mini-instruct]; [MoE-instruct] ; [vision-instruct]
Intended Uses
Primary Use Cases
The model is intended for commercial and research use in multiple languages. The model provides uses for general purpose AI systems and applications which require:
- Memory/compute constrained environments
- Latency bound scenarios
- Strong reasoning (especially code, math and logic)
Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features.
Use Case Considerations
Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.
Release Notes
This is an update over the June 2024 instruction-tuned Phi-3 Mini release based on valuable user feedback. The model used additional post-training data leading to substantial gains on multilingual, multi-turn conversation quality, and reasoning capability. We believe most use cases will benefit from this release, but we encourage users to test in their particular AI applications. We appreciate the enthusiastic adoption of the Phi-3 model family, and continue to welcome all feedback from the community.
You can find more details here: https://huggingface.co/microsoft/Phi-3.5-mini-instruct