metadata

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

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))