AtomicChat/Phi-4-mini-instruct-GGUF

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<div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;"> <a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Phi-4-mini-instruct-GGUF/resolve/main/pillatomicv3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a> <a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Phi-4-mini-instruct-GGUF/resolve/main/pilldiscordv3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a> <a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Phi-4-mini-instruct-GGUF/resolve/main/pillgithubv3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a> </div>

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<img src="https://huggingface.co/AtomicChat/Phi-4-mini-instruct-GGUF/resolve/main/hero.png" alt="Phi 4 Mini" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

<div style="display:flex; justify-content:center; gap:0.5em;"> <a href="https://huggingface.co/microsoft/Phi-4-mini-instruct"><strong>Base model: microsoft/Phi-4-mini-instruct</strong></a> </div> </center>

Phi 4 Mini, self-quantized to GGUF by Atomic Chat. Built straight from Microsoft's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

• —3.8B parameters: the weights this repo quantizes.
• —Context length: 131,072 tokens (128K), as published by Microsoft.
• —32 layers: Dense decoder, hybrid sliding-window (262144) and global attention.
• —Full imatrix ladder: every quant is calibrated with an importance matrix.

[!NOTE] These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

[!IMPORTANT] Always pass --jinja so the Phi 4 Mini chat template is applied. Without it the model can emit malformed turns.

Model Overview

Choosing a quant

[!TIP] Pick the largest file that fits your (V)RAM with room for context. Q4_K_M or UD-Q4_K_XL is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Phi 4 Mini locally with:

• —[Atomic Chat](https://atomic.chat): the easiest path. Open the app, search AtomicChat/Phi-4-mini-instruct-GGUF, pick a quant, hit Use this model.
• —llama.cpp: llama-server -hf AtomicChat/Phi-4-mini-instruct-GGUF:Q4_K_M --jinja -c 8192
• —Ollama: ollama run hf.co/AtomicChat/Phi-4-mini-instruct-GGUF:Q4_K_M
• —LM Studio / Jan: search the repo id, download any quant.

Best practices

Microsoft's recommended sampling configuration for microsoft/Phi-4-mini-instruct.

Run in llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/Phi-4-mini-instruct-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. 1.Download microsoft/Phi-4-mini-instruct (original weights).
2. 2.Convert to f16 GGUF with llama.cpp.
3. 3.Build an importance matrix over our calibration corpus.
4. 4.Quantize the ladder with --imatrix.
5. 5.UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Original model by Microsoft, released under the MIT license. Full terms: MIT. Quantized by Atomic Chat.