Instructions to use simraann/ExplainIt-Phi-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use simraann/ExplainIt-Phi-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="simraann/ExplainIt-Phi-GGUF",
	filename="ExplainIt-Phi-Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use simraann/ExplainIt-Phi-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf simraann/ExplainIt-Phi-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf simraann/ExplainIt-Phi-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 simraann/ExplainIt-Phi-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf simraann/ExplainIt-Phi-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 simraann/ExplainIt-Phi-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf simraann/ExplainIt-Phi-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 simraann/ExplainIt-Phi-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf simraann/ExplainIt-Phi-GGUF:Q4_K_M

Use Docker

docker model run hf.co/simraann/ExplainIt-Phi-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use simraann/ExplainIt-Phi-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "simraann/ExplainIt-Phi-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "simraann/ExplainIt-Phi-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/simraann/ExplainIt-Phi-GGUF:Q4_K_M

Ollama
How to use simraann/ExplainIt-Phi-GGUF with Ollama:
```
ollama run hf.co/simraann/ExplainIt-Phi-GGUF:Q4_K_M
```

Unsloth Studio new

How to use simraann/ExplainIt-Phi-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 simraann/ExplainIt-Phi-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 simraann/ExplainIt-Phi-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use simraann/ExplainIt-Phi-GGUF with Docker Model Runner:
```
docker model run hf.co/simraann/ExplainIt-Phi-GGUF:Q4_K_M
```

Lemonade

How to use simraann/ExplainIt-Phi-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull simraann/ExplainIt-Phi-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ExplainIt-Phi-GGUF-Q4_K_M

List all available models

lemonade list

Model Card for ExplainIt-Phi-GGUF

This repository contains GGUF versions of a microsoft/phi-2 model fine-tuned using QLoRA to explain complex topics in simple, ELI5-style terms.

Model Overview

ExplainIt-Phi is a 2.7B parameter causal language model designed to be a clear and concise explainer. It was fine-tuned on a curated subset of the ELI5 dataset to excel at breaking down complex ideas.

Language(s): English
GitHub Repo: https://github.com/Simran32909/ExplainIt-Phi/

Intended Uses & Limitations

This model is intended for direct use as a question-answering assistant. It is well-suited for generating content for educational materials, blogs, and chatbots. For best results, prompts should follow the format: Instruct: <your question>\nOutput:.

The model is not designed for creative writing or complex multi-turn conversations and may reflect the biases of its training data (the ELI5 subreddit). Always fact-check critical outputs.

How to Get Started

These GGUF models are designed for use with llama.cpp.

Download a model file: Q4_K_M is recommended for general use.

Run with llama.cpp:

./llama-cli -m ./ExplainIt-Phi-Q4_K_M.gguf -p "Instruct: Why is the sky blue?\nOutput:" -n 256

Available Files

This repository provides multiple quantization levels to suit different hardware needs.

File Name	Quantization	Use Case
`ExplainIt-Phi-Q4_K_M.gguf`	Q4_K_M (4-bit)	Default. Balanced quality and size.
`ExplainIt-Phi-Q5_K_M.gguf`	Q5_K_M (5-bit)	Higher quality for systems with more RAM.
`ExplainIt-Phi-Q8_0.gguf`	Q8_0 (8-bit)	Near-lossless, best for GPU execution.

Evaluation: Before vs. After

The fine-tuning process significantly improved the model's ability to provide simple, analogy-driven explanations.

Prompt: What is an API and what does it do, in simple terms?

Base Phi-2 Model (Before)	Fine-Tuned ExplainIt-Phi (After)
"An API, or Application Programming Interface, is a set of rules and protocols that allows different software applications to communicate with each other. It acts as a bridge between two applications, allowing them to exchange data and functionality."	"An API is like a waiter in a restaurant. You (an application) don't need to know how the kitchen works. You just give your order (a request) to the waiter (the API), and the waiter brings you your food (the data)."

Training Details

The model was fine-tuned using the QLoRA technique on a curated subset of the sentence-transformers/eli5 dataset. For a full breakdown of the training procedure, hyperparameters, and infrastructure, please see the project's GitHub repository.

Downloads last month: 20

GGUF

Model size

3B params

Architecture

phi2

Hardware compatibility

4-bit

5-bit

8-bit

Model tree for simraann/ExplainIt-Phi-GGUF

Base model

microsoft/phi-2

Quantized

(58)

this model

simraann
/

ExplainIt-Phi-GGUF