Platform This includes various technologies such as Azure AI Studio, Azure Machine Learning, AI Tools, Kaito, and ONNX Runtime.
Infrastructure This includes the CPU and FPGA, which are essential for the fine-tuning process. Let me show you the icons for each of these technologies.
Tools & Framework This includes ONNX Runtime and ONNX Runtime. Let me show you the icons for each of these technologies. [Insert icons for ONNX Runtime and ONNX Runtime]
The fine-tuning process with Microsoft technologies involves various components and tools. By understanding and utilizing these technologies, we can effectively fine-tune our applications and create better solutions.
Fine-tune the model using hosted fine-tuning, without the need to create and manage compute.
Serverless fine-tuning is available for Phi-3-mini and Phi-3-medium models, enabling developers to quickly and easily customize the models for cloud and edge scenarios without having to arrange for compute. We have also announced that, Phi-3-small, is now available through our Models-as-a-Service offering so developers can quickly and easily get started with AI development without having to manage underlying infrastructure.
Users manage their own compute in order to Fine-tune their models.
| Scenario | LoRA | QLoRA | PEFT | DeepSpeed | ZeRO | DORA |
| Adapting pre-trained LLMs to specific tasks or domains | Yes | Yes | Yes | Yes | Yes | Yes |
| Fine-tuning for NLP tasks such as text classification, named entity recognition, and machine translation | Yes | Yes | Yes | Yes | Yes | Yes |
| Fine-tuning for QA tasks | Yes | Yes | Yes | Yes | Yes | Yes |
| Fine-tuning for generating human-like responses in chatbots | Yes | Yes | Yes | Yes | Yes | Yes |
| Fine-tuning for generating music, art, or other forms of creativity | Yes | Yes | Yes | Yes | Yes | Yes |
| Reducing computational and financial costs | Yes | Yes | No | Yes | Yes | No |
| Reducing memory usage | No | Yes | No | Yes | Yes | Yes |
| Using fewer parameters for efficient finetuning | No | Yes | Yes | No | No | Yes |
| Memory-efficient form of data parallelism that gives access to the aggregate GPU memory of all the GPU devices available | No | No | No | Yes | Yes | Yes |
