Instructions to use michael10098/phi3-mini-q4_k_m_aviation_museum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use michael10098/phi3-mini-q4_k_m_aviation_museum with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="michael10098/phi3-mini-q4_k_m_aviation_museum",
	filename="phi3-mini-q4_k_m_aviation_museum.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 michael10098/phi3-mini-q4_k_m_aviation_museum 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 michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION
# Run inference directly in the terminal:
llama cli -hf michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION
# Run inference directly in the terminal:
llama cli -hf michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION

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 michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION
# Run inference directly in the terminal:
./llama-cli -hf michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION

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 michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION
# Run inference directly in the terminal:
./build/bin/llama-cli -hf michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION

Use Docker

docker model run hf.co/michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION

LM Studio
Jan

Ollama

How to use michael10098/phi3-mini-q4_k_m_aviation_museum with Ollama:

ollama run hf.co/michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION

Unsloth Studio

How to use michael10098/phi3-mini-q4_k_m_aviation_museum 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 michael10098/phi3-mini-q4_k_m_aviation_museum 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 michael10098/phi3-mini-q4_k_m_aviation_museum to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for michael10098/phi3-mini-q4_k_m_aviation_museum to start chatting

Atomic Chat new
Docker Model Runner
How to use michael10098/phi3-mini-q4_k_m_aviation_museum with Docker Model Runner:
```
docker model run hf.co/michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION
```

Lemonade

How to use michael10098/phi3-mini-q4_k_m_aviation_museum with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull michael10098/phi3-mini-q4_k_m_aviation_museum:Q4_K_M_AVIATION

Run and chat with the model

lemonade run user.phi3-mini-q4_k_m_aviation_museum-Q4_K_M_AVIATION

List all available models

lemonade list

✈️ Phi-3 Military Aviation Museum Guide

A fine-tuned version of Phi-3 Mini trained on 2,500 military aviation Q&A pairs. Answers questions in the style of an enthusiastic aircraft museum guide.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("michael10098/phi3-mini-q4_k_m_aviation_museum", torch_dtype=torch.float16, device_map="cuda")
tokenizer = AutoTokenizer.from_pretrained("michael10098/phi3-mini-q4_k_m_aviation_museum")

prompt = "<|system|>\nYou are an enthusiastic aviation museum guide.<|end|>\n<|user|>\nWhat is a B-29?<|end|>\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Training Details

Base model: Phi-3 Mini 4k Instruct
Method: QLoRA (4-bit quantization + LoRA)
Dataset: 2,500 military aviation Q&A pairs
Hardware: NVIDIA RTX 4060 (8GB VRAM)

Downloads last month: 45

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

4-bit

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Model tree for michael10098/phi3-mini-q4_k_m_aviation_museum

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