AtomicChat/Phi-4-mini-instruct-GGUF overview

---

license: mit

license_link: https://huggingface.co/microsoft/Phi-4-mini-instruct/resolve/main/LICENSE

thumbnail: https://huggingface.co/AtomicChat/Phi-4-mini-instruct-GGUF/resolve/main/hero.png

base_model:

• microsoft/Phi-4-mini-instruct

base_model_relation: quantized

quantized_by: AtomicChat

pipeline_tag: text-generation

library_name: gguf

tags:

• atomic-chat
• phi
• phi4
• microsoft
• gguf
• llama.cpp
• quantized

---

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<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/pill_atomic_v3.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/pill_discord_v3.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/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>

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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;"/>

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<a href="https://huggingface.co/microsoft/Phi-4-mini-instruct"><strong>Base model: microsoft/Phi-4-mini-instruct</strong></a>

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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.