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AtomicChat/Phi-4-mini-instruct-GGUF

🤗 Hugging Face sourcetext-generationmit15 GBGGUF✓ 5 checksumsupdated today
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Base model: microsoft/Phi-4-mini-instruct

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

Property Value
Base model microsoft/Phi-4-mini-instruct
Parameters 3.8B
Layers 32
Sliding window 262144 tokens
Context length 131,072 tokens (128K)
Vocabulary 200,064
Modalities Text
Architecture Dense decoder, hybrid sliding-window (262144) and global attention, 24 attention heads over 8 KV heads, Phi3ForCausalLM
This repo GGUF quants (imatrix). Quants: Q4_K_M, UD-Q4_K_XL, Q5_K_M, Q6_K, Q8_0

Choosing a quant

Quant Size Notes
Q4_K_M 2.5 GB Recommended default. Best balance of size, speed and quality.
UD-Q4_K_XL 2.6 GB Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint.
Q5_K_M 2.8 GB Higher quality, low loss.
Q6_K 3.2 GB Near lossless, noticeably lighter than Q8_0.
Q8_0 4.1 GB Effectively lossless, reference quality.

[!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: 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

Parameter Value
temperature 0.0

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. Download microsoft/Phi-4-mini-instruct (original weights).
  2. Convert to f16 GGUF with llama.cpp.
  3. Build an importance matrix over our calibration corpus.
  4. Quantize the ladder with --imatrix.
  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.