---
language: en
license: mit
tags:
- phi-2
- peft
- lora
- fine-tuned
- neuroscience
---

# Neuroscience Fine-tuned Phi-2 Model

## Model Description

This is a fine-tuned version of Microsoft's Phi-2 model, adapted specifically for neuroscience domain content.

## Training Procedure

- **Base Model**: Microsoft Phi-2 (2.7B parameters)
- **Training Type**: LoRA fine-tuning
- **Training Dataset**: BrainGPT/train_valid_split_pmc_neuroscience_2002-2022_filtered_subset
- **Training Duration**: 3+ epochs
- **Parameter-Efficient Fine-Tuning**: Used LoRA with r=16, alpha=32

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto")

# Load adapter
model = PeftModel.from_pretrained(base_model, "alaamostafa/Microsoft-Phi-2")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2")

# Generate text
input_text = "Recent advances in neuroscience suggest that"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```