llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)✈️ 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
- 38
Hardware compatibility
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Base model
microsoft/Phi-3-mini-4k-instruct
# !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", )