Instructions to use tripathyShaswata/Phi-tiny-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 tripathyShaswata/Phi-tiny-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 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 serve -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/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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 Desktop
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

Atomic Chat

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: 178

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

(2)

this model