Instructions to use AtomicChat/Phi-4-mini-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AtomicChat/Phi-4-mini-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AtomicChat/Phi-4-mini-instruct-GGUF",
	filename="Phi-4-mini-instruct-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AtomicChat/Phi-4-mini-instruct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use AtomicChat/Phi-4-mini-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/Phi-4-mini-instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AtomicChat/Phi-4-mini-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL

Ollama
How to use AtomicChat/Phi-4-mini-instruct-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use AtomicChat/Phi-4-mini-instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AtomicChat/Phi-4-mini-instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AtomicChat/Phi-4-mini-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AtomicChat/Phi-4-mini-instruct-GGUF to start chatting

How to use AtomicChat/Phi-4-mini-instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AtomicChat/Phi-4-mini-instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AtomicChat/Phi-4-mini-instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use AtomicChat/Phi-4-mini-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL
```

Lemonade

How to use AtomicChat/Phi-4-mini-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/Phi-4-mini-instruct-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Phi-4-mini-instruct-GGUF-UD-Q4_K_XL

List all available models

lemonade list

worthant commited on 3 days ago

Commit

3169583

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1 Parent(s): 9b8a422

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README.md +121 -0

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	@@ -0,0 +1,121 @@

+---
+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
+---
+<center>
+<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/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>
+</div>
+<br/>
+<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](https://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](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
+| Parameter | Value |
+|---|---|
+| temperature | 0.0 |
+Microsoft's recommended sampling configuration for `microsoft/Phi-4-mini-instruct`.
+## Run in llama.cpp
+```bash
+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
+```
+```bash
+./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](https://github.com/ggml-org/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](https://huggingface.co/microsoft/Phi-4-mini-instruct/resolve/main/LICENSE). Quantized by Atomic Chat.