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

Libraries

How to use tripathyShaswata/Phi-tiny-MoE-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tripathyShaswata/Phi-tiny-MoE-instruct-GGUF",
	filename="Phi-tiny-MoE-instruct-Q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tripathyShaswata/Phi-tiny-MoE-instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0

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 tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0

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 tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0

Use Docker

docker model run hf.co/tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use tripathyShaswata/Phi-tiny-MoE-instruct-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0

Ollama
How to use tripathyShaswata/Phi-tiny-MoE-instruct-GGUF with Ollama:
```
ollama run hf.co/tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0
```

Unsloth Studio new

How to use tripathyShaswata/Phi-tiny-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 tripathyShaswata/Phi-tiny-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 tripathyShaswata/Phi-tiny-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 tripathyShaswata/Phi-tiny-MoE-instruct-GGUF to start chatting

Docker Model Runner
How to use tripathyShaswata/Phi-tiny-MoE-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0
```

Lemonade

How to use tripathyShaswata/Phi-tiny-MoE-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tripathyShaswata/Phi-tiny-MoE-instruct-GGUF:Q8_0

Run and chat with the model

lemonade run user.Phi-tiny-MoE-instruct-GGUF-Q8_0

List all available models

lemonade list

Phi-tiny-MoE-instruct GGUF

GGUF quantized version of microsoft/Phi-tiny-MoE-instruct for local inference with llama.cpp, Ollama, LM Studio, and GPT4All.

Phi-tiny-MoE is Microsoft's efficient Mixture-of-Experts language model — 16 experts, 2 active per token — delivering strong instruction-following performance in a compact, fast package.

Available Quantizations

File	Quant	Size	RAM Needed	Quality
`Phi-tiny-MoE-instruct-Q8_0.gguf`	Q8_0	4.0 GB	~6 GB	Near-lossless

How to Use

With llama.cpp

./llama-cli -m Phi-tiny-MoE-instruct-Q8_0.gguf -p "Explain quantum computing in simple terms" -n 512

With Ollama

echo 'FROM ./Phi-tiny-MoE-instruct-Q8_0.gguf' > Modelfile
ollama create phi-tiny-moe -f Modelfile
ollama run phi-tiny-moe

With LM Studio

Download the Q8_0 file
Open LM Studio → Load Model → Select the file
Start chatting

Model Details

Architecture: PhiMoE (Mixture of Experts)
Total Experts: 16
Active Experts per Token: 2
Hidden Size: 4096
Layers: 32
Attention Heads: 16
Context Length: 4096 tokens
License: MIT

Original Model

Built by Microsoft Research. See the original at microsoft/Phi-tiny-MoE-instruct.

Quantized by

Shaswata Tripathy | GitHub | Medium | LinkedIn | Hugging Face

Downloads last month: 165

GGUF

Model size

4B params

Architecture

phimoe

Hardware compatibility

8-bit

Model tree for tripathyShaswata/Phi-tiny-MoE-instruct-GGUF

Base model

microsoft/Phi-tiny-MoE-instruct

Quantized

(1)

this model