Instructions to use AlessandroW/Phi-3-mini-128k-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlessandroW/Phi-3-mini-128k-instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AlessandroW/Phi-3-mini-128k-instruct-gguf",
	filename="Phi-3-mini-128k-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 AlessandroW/Phi-3-mini-128k-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 AlessandroW/Phi-3-mini-128k-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AlessandroW/Phi-3-mini-128k-instruct-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AlessandroW/Phi-3-mini-128k-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AlessandroW/Phi-3-mini-128k-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 AlessandroW/Phi-3-mini-128k-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AlessandroW/Phi-3-mini-128k-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 AlessandroW/Phi-3-mini-128k-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AlessandroW/Phi-3-mini-128k-instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/AlessandroW/Phi-3-mini-128k-instruct-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use AlessandroW/Phi-3-mini-128k-instruct-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlessandroW/Phi-3-mini-128k-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": "AlessandroW/Phi-3-mini-128k-instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AlessandroW/Phi-3-mini-128k-instruct-gguf:Q4_K_M

Ollama
How to use AlessandroW/Phi-3-mini-128k-instruct-gguf with Ollama:
```
ollama run hf.co/AlessandroW/Phi-3-mini-128k-instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use AlessandroW/Phi-3-mini-128k-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 AlessandroW/Phi-3-mini-128k-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 AlessandroW/Phi-3-mini-128k-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 AlessandroW/Phi-3-mini-128k-instruct-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use AlessandroW/Phi-3-mini-128k-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/AlessandroW/Phi-3-mini-128k-instruct-gguf:Q4_K_M
```

Lemonade

How to use AlessandroW/Phi-3-mini-128k-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AlessandroW/Phi-3-mini-128k-instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Phi-3-mini-128k-instruct-gguf-Q4_K_M

List all available models

lemonade list

Model Summary

This repo provides the GGUF format for the Phi-3-Mini-128K-Instruct.

For more details check out the original model at microsoft/Phi-3-mini-128k-instruct.

The Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. The model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K which is the context length (in tokens) that it can support.

After initial training, the model underwent a post-training process that involved supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures. When evaluated against benchmarks that test common sense, language understanding, mathematics, coding, long-term context, and logical reasoning, the Phi-3 Mini-128K-Instruct demonstrated robust and state-of-the-art performance among models with fewer than 13 billion parameters. Resources and Technical Documentation:

Resources and Technical Documentation:

This repo provides GGUF files and Llamafiles (d228e01d) for the Phi-3 Mini-128K-Instruct model.

Name	Quant method	Bits	Size	Use case
Phi-3-mini-128k-instruct-Q4_K_M.gguf	Q4_K_M	4	2.39 GB	medium, balanced quality - recommended
Phi-3-mini-128k-instruct-Q4_K_M.llamafile	Q4_K_M	4	2.4 GB	medium, balanced quality - recommended
Phi-3-mini-128k-instruct-f16.gguf	None	16	7.64 GB	minimal quality loss
Phi-3-mini-128k-instruct-f16.llamafile	None	16	7.65 GB	minimal quality loss

Note: When using the llamafile version make sure to specify the context size, e.g., ./Phi-3-mini-128k-instruct-Q4_K_M.llamafile -c 0 -p "your prompt".

License

The model is licensed under the MIT license.

Downloads last month: 69

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

4-bit

16-bit

Model tree for AlessandroW/Phi-3-mini-128k-instruct-gguf

Base model

microsoft/Phi-3-mini-128k-instruct

Quantized

(86)

this model