v2.2 · 2026-06-12

Phi-4 Mini Instruct

phi-4-mini-instruct

TEXT-CHAT

Phi-4 Mini Instruct is a 3.8B text chat model profile for local AI planning. This page records provider, license, context, quantization, hardware-fit estimates, setup hints, source links, and caveats so readers do not choose a model by name alone.

PROVIDER

Microsoft

FAMILY

Phi-4

MODEL TYPE

text-chat

PARAMETERS

3.8B

MODALITIES

text

ARCHITECTURE

Dense decoder-only Transformer; GQA; 200K vocabulary; shared input-output embeddings

CONTEXT WINDOW

128,000 tokens

TRAINING TOKENS

—

RELEASE DATE

2025-02

LICENSE & LINKS

MITlicense text

MODEL CARD

huggingface.co/microsoft/Phi-4-mini-instruct

HUGGING FACE

microsoft/Phi-4-mini-instruct

GGUF REPOSITORIES

bartowski/microsoft_Phi-4-mini-instruct-GGUF community conversion

Community GGUF repository referenced by the June 2026 model research.

SETUP HINTS

OLLAMA

ollama run phi4-mini

LM STUDIO

Available according to the supplied June 2026 research; select a cited GGUF/MLX build and verify current app metadata before relying on hands-on setup guidance.

LLAMA.CPP

Use the cited GGUF repository (bartowski/microsoft_Phi-4-mini-instruct-GGUF) with current llama.cpp-compatible tooling.

QUANTIZATION FILE SIZES (GGUF)

Q4_K_M

2.49 GB

Q5_K_M

2.85 GB

Q8_0

4.08 GB

Quantization note: Q4_K_M for first local attempts unless a cited runtime recommends a different default.

RAM / VRAM ESTIMATES

MIN RAM

8 GB

COMFORTABLE RAM

16 GB

MIN VRAM

8 GB

COMFORTABLE VRAM

16 GB

These are conservative local-inference estimates, not official hardware requirements.

HARDWARE FIT

CPU only (no GPU)

limited

8 GB RAM

limited

16 GB RAM

comfortable

32 GB RAM

comfortable

8 GB VRAM

limited

12 GB VRAM

limited

24 GB VRAM

comfortable

Apple Silicon (unified memory)

comfortable

Hardware fit values are conservative local-inference estimates based on GGUF size plus runtime and KV-cache overhead. Actual requirements depend on context length, quantization, runtime, GPU offload, and other running apps.

BEST FOR

·Fast local chat, function-calling tests, multilingual prompts, and long-context experiments on modest consumer hardware today.

AVOID IF

·Choose something larger when factual knowledge, creative writing quality, or nuanced coding depth matters more.

CAVEATS

·Function calling is experimental and may hallucinate function names, URLs, or invalid tool calls. Long-context use can require substantially more memory than the quantized file size suggests. Larger Qwen, Gemma, and Mistral models may outperform it on broad general tasks.

·Commercial-use status from the supplied research: Permitted under MIT license; Microsoft describes the model as intended for commercial and research use.

·Beginner summary from supplied research: Phi-4 Mini Instruct is Microsoft’s practical small Phi model for beginners who want fast local chat, multilingual basics, long context, and function-calling experiments without moving into heavier 7B, 8B, or 14B model territory on laptops.

SOURCE URLS