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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use gabriellarson/Phi-mini-MoE-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 gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M

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 gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M

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 gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use gabriellarson/Phi-mini-MoE-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gabriellarson/Phi-mini-MoE-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": "gabriellarson/Phi-mini-MoE-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M

Ollama
How to use gabriellarson/Phi-mini-MoE-instruct-GGUF with Ollama:
```
ollama run hf.co/gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use gabriellarson/Phi-mini-MoE-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 gabriellarson/Phi-mini-MoE-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 gabriellarson/Phi-mini-MoE-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 gabriellarson/Phi-mini-MoE-instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use gabriellarson/Phi-mini-MoE-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M
```

Lemonade

How to use gabriellarson/Phi-mini-MoE-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gabriellarson/Phi-mini-MoE-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Phi-mini-MoE-instruct-GGUF-Q4_K_M

List all available models

lemonade list

nielsr HF Staff commited on Jun 29, 2025

Commit

1bffe74

verified ·

1 Parent(s): 2e34b40

Add Github URL and library name

Browse files

This PR adds the link to the Github repository and the library name (transformers).

Files changed (1) hide show

README.md +6 -4

README.md CHANGED Viewed

@@ -1,12 +1,13 @@
 ---
-license: mit
 language:
 - en
 context_length:
 - 4k
-pipeline_tag: text-generation
-base_model:
-- microsoft/Phi-mini-MoE-instruct
 ---
 ## my suggested samplers:
@@ -17,6 +18,7 @@ base_model:
 Phi-mini-MoE is a lightweight Mixture of Experts (MoE) model with 7.6B total parameters and 2.4B activated parameters. It is compressed and distilled from the base model shared by [Phi-3.5-MoE](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) and [GRIN-MoE](https://huggingface.co/microsoft/GRIN-MoE) using the [SlimMoE](https://arxiv.org/pdf/2506.18349) approach, then post-trained via supervised fine-tuning and direct preference optimization for instruction following and safety. The model is trained on Phi-3 synthetic data and filtered public documents, with a focus on high-quality, reasoning-dense content. It is part of the SlimMoE series, which includes a smaller variant, [Phi-tiny-MoE](https://huggingface.co/microsoft/Phi-tiny-MoE-instruct), with 3.8B total and 1.1B activated parameters.
 References: <br>
 📖 [SlimMoE](https://arxiv.org/pdf/2506.18349) <br>

 ---
+base_model:
+- microsoft/Phi-mini-MoE-instruct
 language:
 - en
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
 context_length:
 - 4k
 ---
 ## my suggested samplers:
 Phi-mini-MoE is a lightweight Mixture of Experts (MoE) model with 7.6B total parameters and 2.4B activated parameters. It is compressed and distilled from the base model shared by [Phi-3.5-MoE](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) and [GRIN-MoE](https://huggingface.co/microsoft/GRIN-MoE) using the [SlimMoE](https://arxiv.org/pdf/2506.18349) approach, then post-trained via supervised fine-tuning and direct preference optimization for instruction following and safety. The model is trained on Phi-3 synthetic data and filtered public documents, with a focus on high-quality, reasoning-dense content. It is part of the SlimMoE series, which includes a smaller variant, [Phi-tiny-MoE](https://huggingface.co/microsoft/Phi-tiny-MoE-instruct), with 3.8B total and 1.1B activated parameters.
+Code for SlimMoE: https://github.com/microsoft/torchscale/tree/main/examples/moe
 References: <br>
 📖 [SlimMoE](https://arxiv.org/pdf/2506.18349) <br>