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phi3-mini-q4_k_m_aviation_museum
michael10098/phi3-mini-q4_k_m_aviation_museum
phi3-mini-q4_k_m_aviation_museum is a machine learning model from michael10098. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
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.
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At a glance
- License
- mit
- Access
- Public
- Created
- Jun 30, 2026
- Updated
- Jul 1, 2026
- SHA
- 882a30c0
Base models
Model card
From the Hugging Face model README
✈️ 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)
- License
- mit
- Created
- Jun 30, 2026
- Updated
- Jul 1, 2026