Instructions to use KRAFTYUX/phi3-prompt-engineer-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KRAFTYUX/phi3-prompt-engineer-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="KRAFTYUX/phi3-prompt-engineer-gguf",
	filename="phi3-prompt-engineer-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use KRAFTYUX/phi3-prompt-engineer-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 KRAFTYUX/phi3-prompt-engineer-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KRAFTYUX/phi3-prompt-engineer-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 KRAFTYUX/phi3-prompt-engineer-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KRAFTYUX/phi3-prompt-engineer-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 KRAFTYUX/phi3-prompt-engineer-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KRAFTYUX/phi3-prompt-engineer-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 KRAFTYUX/phi3-prompt-engineer-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KRAFTYUX/phi3-prompt-engineer-gguf:Q4_K_M

Use Docker

docker model run hf.co/KRAFTYUX/phi3-prompt-engineer-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use KRAFTYUX/phi3-prompt-engineer-gguf with Ollama:
```
ollama run hf.co/KRAFTYUX/phi3-prompt-engineer-gguf:Q4_K_M
```

Unsloth Studio

How to use KRAFTYUX/phi3-prompt-engineer-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 KRAFTYUX/phi3-prompt-engineer-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 KRAFTYUX/phi3-prompt-engineer-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KRAFTYUX/phi3-prompt-engineer-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use KRAFTYUX/phi3-prompt-engineer-gguf with Docker Model Runner:
```
docker model run hf.co/KRAFTYUX/phi3-prompt-engineer-gguf:Q4_K_M
```

Lemonade

How to use KRAFTYUX/phi3-prompt-engineer-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KRAFTYUX/phi3-prompt-engineer-gguf:Q4_K_M

Run and chat with the model

lemonade run user.phi3-prompt-engineer-gguf-Q4_K_M

List all available models

lemonade list

Phi-3 Prompt Engineer (GGUF)

This repository contains GGUF quantized versions of the Phi-3 Prompt Engineer model, fine-tuned to excel at refining user requests into detailed, structured prompts for LLMs.

Model Description

Base Model: microsoft/Phi-3-mini-4k-instruct
Fine-tuning: LoRA adapters trained on a custom dataset of prompt refinement examples.
Purpose: To act as a specialized "Prompt Engineer" agent, converting vague user ideas into high-quality, actionable system instructions and prompts.

Available Quantizations

The following GGUF files are available for download. Q4_K_M is recommended for most users as it balances performance and quality perfectly.

Filename	Quantization	Size	SHA256 Checksum
phi3-prompt-engineer-f16.gguf	F16	7.12 GB	`1bc8a41027c400eda38d5dc9cf97fcf0a4617072d2c3c132b922dd2589783c16`
phi3-prompt-engineer-q4_k_m.gguf	Q4_K_M	2.23 GB	`cc840e37e0f21c97bf158211a2f0dda1096294ee47ab3f199da854cc841c39f7`
phi3-prompt-engineer-q5_k_m.gguf	Q5_K_M	2.62 GB	`218b8021b5f1b1efb2e0914d939f5464d8cb790ef9d9e35eaa05e1b8b3cc560f`
phi3-prompt-engineer-q6_k.gguf	Q6_K	2.92 GB	`4cf033bfe14c43ebf84e47f7184f2f0f5286a30a2c87927d59aa95d9b4a455d5`
phi3-prompt-engineer-q8_0.gguf	Q8_0	3.78 GB	`2aa4f14b038c01a50ad8e3306832a25b8c6f704aa1d9efa29a88856dee924fce`

Usage

Ollama

You can use these files directly with Ollama.

Create a Modelfile:

FROM ./phi3-prompt-engineer-q4_k_m.gguf
SYSTEM "You are an Expert Prompt Engineer. Your goal is to refine the following user request into a clear, structured, and detailed prompt."

Create and run the model:

ollama create phi3-pe -f Modelfile
ollama run phi3-pe

llama.cpp

./main -m phi3-prompt-engineer-q4_k_m.gguf -p "You are an Expert Prompt Engineer. Refine this: make a snake game in python"

Training Data

The model was trained on a proprietary dataset (raw_dataset.jsonl) consisting of:

Input: Raw, often vague user requests (e.g., "make a login page").
Output: Detailed, structured Prompt Engineering responses including personas, constraints, and specific requirements.
Focus: Web development, Python scripting, creative writing, and system design tasks.

License

MIT

Downloads last month: 20

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for KRAFTYUX/phi3-prompt-engineer-gguf

Base model

microsoft/Phi-3-mini-4k-instruct

Quantized

(173)

this model