Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.
Official4
microsoft.com2026-03-05officialauthorityHN 93 pts · 11 comments4,049 words
Phi-4-reasoning-vision-15B is a 15 billion parameter open-weight multimodal reasoning model trained with just 200 billion tokens of multimodal data. The model excels at math and science reasoning and understanding user-interfaces, presenting an appealing value relative to popular open-weight models.generated, cited
techcommunity.microsoft.com2024-12-13officialauthorityHN front pageabout the phi 4 familyHN 439 pts · 143 comments5,664 words
Microsoft introduces Phi-4, a 14B parameter small language model designed for complex reasoning in math and language tasks. The model achieves performance through the use of high-quality synthetic datasets, curation of high-quality organic data, and post-training innovations. Phi-4 outperforms much larger models, including Gemini Pro 1.5, on math competition problems.written, reviewed, cited
- Phi-4 is available on Azure AI Foundry and Hugging Face as of the Dec 12, 2024 announcement.
- Developers can integrate content safety features into applications easily through a single API.
- The blog post was updated on Jan 08, 2025, indicating potential changes after the initial release.
azure.microsoft.com2025-05-01officialauthorityHN front pageHN 131 pts · 26 comments1,007 words
Microsoft releases Phi-4-reasoning, a 14-billion parameter open-weight model, alongside Phi-4-reasoning-plus and Phi-4-mini-reasoning on Azure AI Foundry and HuggingFace. Phi-4-reasoning-plus utilizes 1.5x more tokens than Phi-4-reasoning to deliver higher accuracy, while both models outperform the 671-billion parameter DeepSeek-R1 on the AIME 2025 test. Optimized versions using ONNX are now available for Snapdragon powered Copilot+ PCs to run efficiently on NPUs.written, reviewed, cited
- Phi-4-reasoning is trained via supervised fine-tuning of Phi-4 on carefully curated reasoning demonstrations from OpenAI o3-mini.
- Phi-4-mini-reasoning is fine-tuned with synthetic data generated by Deepseek-R1 model to balance efficiency with advanced reasoning ability.
- The NPU-optimized Phi Silica variant is designed to be preloaded in memory for blazing fast time to first token responses.
microsoft.com2025-05-01officialauthorityHN 2 pts · 1 comments
Analysis6
unsloth.ai2025-01-10authorityHN front pageabout the phi 4 familyHN 193 pts · 68 comments
ollama.com2025-01-09HN front pageabout the phi 4 familyHN 291 pts · 135 comments220 words
Phi-4 is a 14B parameter open model designed for memory/compute constrained environments and latency bound scenarios. It supports a context length of 16k tokens and is specifically optimized for reasoning and logic tasks. The model focuses primarily on English, requiring developers to evaluate accuracy and safety before deployment in high-risk scenarios.written, reviewed, cited
simonwillison.net2024-12-16authorityabout the phi 4 familyHN 5 pts · 0 comments740 words
techcrunch.com2024-12-13about the phi 4 familyHN 24 pts · 1 comments
arxiv.org2024-12-13authorityabout the phi 4 familyHN 3 pts · 0 comments
venturebeat.com2025-01-09about the phi 4 familyHN 3 pts · 1 comments
