microsoft/Phi-4-multimodal-instruct

automatic-speech-recognition model by microsoft

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🎉Phi-4: [mini-reasoning | reasoning] | [multimodal-instruct | onnx]; [mini-instruct | onnx] Phi-4-multimodal-instruct is a lightweight open multimodal foundation model that leverages the language, vision, and speech research and datasets used for Phi-3.5 and 4.0 models. The model processes text, image, and audio inputs, generating text outputs, and comes with 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning, direct preference optimization and RLHF (Reinforcement Learning from Human Feedback) to support precise instruction adherence and safety measures. The languages that each modal supports are the following: French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian 📰 Phi-4-multimodal Microsoft Blog 📖 Phi-4-multimodal Technical Report 🏡 Phi Portal 👩‍🍳 Phi Cookbook 🖥️ Try It on Azure, GitHub, Nvidia, Huggingface playgrounds 📱Huggingface Spaces Thoughts Organizer, Stories Come Alive, Phine Speech Translator Watch as Phi-4 Multimodal analyzes spoken language to help plan a…

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Model overview

microsoft/Phi-4-multimodal-instruct is a automatic-speech-recognition model repository published by microsoft on Hugging Face. The source reports the transformers library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task: automatic-speech-recognition
Library: transformers
Recent downloads (30 days): 266,198
Cumulative likes: 1,615
Hugging Face trending score: 0
Reported safetensors parameters: 5,574,460,384
Architecture: Phi4MMForCausalLM
License: mit
Access: Not gated by Hugging Face
Created: 2025-02-24T22:33:32.000Z
Last modified: 2025-12-10T20:18:10.000Z

Compatibility and lineage

Reported languages: multilingual, ar, zh, cs, da, nl, en, fi, fr, de, he, hu, it, ja, ko, no, pl, pt, ru, es, sv, th, tr, uk

Base-model lineage was not included.

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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