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@rishibommasani - add phi-4-1 #257

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30 changes: 30 additions & 0 deletions assets/microsoft.yaml
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Expand Up @@ -1032,3 +1032,33 @@
use case, particularly for high risk scenarios.
monitoring: Unknown
feedback: Unknown
- type: model
name: Phi-4
organization: Microsoft
description: "Phi-4 is the latest member of our Phi family of small language models and demonstrates what’s possible as we continue to probe the boundaries of SLMs. Phi-4 outperforms comparable and larger models on math related reasoning due to advancements throughout the processes, including the use of high-quality synthetic datasets, curation of high-quality organic data, and post-training innovations."
created_date: 2024-12-13
url: https://aka.ms/phi4blog
model_card: unknown
modality:
explanation: "Phi-4, the latest small language model in Phi family, that offers high quality results at a small size (14B parameters)."
value: text; text
analysis: "Phi-4 outperforms comparable and larger models on math related reasoning due to advancements throughout the processes, including the use of high-quality synthetic datasets, curation of high-quality organic data, and post-training innovations."
size:
explanation: "Phi-4, the latest small language model in Phi family, that offers high quality results at a small size (14B parameters)."
value: 14B parameters
dependencies: []
training_emissions: unknown
training_time: unknown
training_hardware: unknown
quality_control: "Azure AI evaluations in AI Foundry enable developers to iteratively assess the quality and safety of models and applications using built-in and custom metrics to inform mitigations."
access:
explanation: "Phi-4 is currently available on Azure AI Foundry under a Microsoft Research License Agreement (MSRLA) and will be available on Hugging Face next week."
value: limited
license:
explanation: "Phi-4 is currently available on Azure AI Foundry under a Microsoft Research License Agreement (MSRLA)"
value: Microsoft Research License Agreement (MSRLA)
intended_uses: Complex reasoning in areas such as math, and conventional language processing.
prohibited_uses: unknown
monitoring: "Once in production, developers can monitor their application for quality and safety, adversarial prompt attacks, and data integrity, making timely interventions with the help of real-time alerts."
feedback: unknown

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