Shivenys/phi3-mini-lora-tutorial

Phi-3 Mini 4K Instruct - LoRA Fine-Tuned Model

This repository contains a QLoRA (4-bit LoRA) adapter fine-tuned from Microsoft Phi-3 Mini 4K Instruct using the Hugging Face Transformers, PEFT, and TRL libraries.

Note: This model was created as a learning project to understand the complete LLM fine-tuning workflow.

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Base Model

• Base Model: microsoft/Phi-3-mini-4k-instruct
• Fine-Tuning Method: QLoRA (4-bit Quantization + LoRA)
• Framework: Hugging Face Transformers
• PEFT Library: PEFT
• Trainer: TRL SFTTrainer

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Model Details

Parameter	Value
Base Model	Phi-3 Mini 4K Instruct
Fine-Tuning Method	QLoRA
LoRA Rank (r)	8
LoRA Alpha	16
LoRA Dropout	0.05
Task	Causal Language Modeling
Quantization	4-bit NF4
Compute Type	FP16

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Training Configuration

• Epochs: 5
• Batch Size: 1
• Learning Rate: 2e-4
• Max Sequence Length: 256
• Optimizer: AdamW (default TRL optimizer)

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Training Dataset

The adapter was fine-tuned on a small custom instruction-response dataset containing sample examples such as:

• Who created Python?
• What is AI?
• What is LoRA?
• What is the capital of France?

This dataset was intentionally kept small for educational purposes to demonstrate the end-to-end fine-tuning process.

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Usage

Install the required libraries:

pip install transformers peft bitsandbytes accelerate torch

Load the adapter:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

BASE_MODEL = "microsoft/Phi-3-mini-4k-instruct"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    quantization_config=bnb_config,
    device_map="auto",
)

model = PeftModel.from_pretrained(
    base_model,
    "YOUR_USERNAME/YOUR_REPOSITORY_NAME"
)

Generate text:

prompt = """### Instruction:
Who created Python?

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=100,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Repository Structure

adapter_config.json
adapter_model.safetensors
README.md

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Limitations

• This adapter was trained on a very small dataset.
• It is intended for educational and demonstration purposes.
• It should not be considered a production-ready fine-tuned model.

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Acknowledgements

• Microsoft for the Phi-3 Mini 4K Instruct model
• Hugging Face Transformers
• Hugging Face TRL
• PEFT
• BitsAndBytes

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License

This repository contains only the LoRA adapter.

Please follow the license terms of the original Phi-3 Mini 4K Instruct model when using the base model.