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# Fine-Tuned GPT-2 Model for Instruction-Based Tasks
This model is a fine-tuned version of GPT-2, adapted for instruction-based tasks. It has been trained to provide helpful and coherent responses to a variety of prompts.
## Model Description
This model is based on OpenAI's GPT-2 architecture and has been fine-tuned to respond to instructions in a format that mimics conversational exchanges. The fine-tuning process enhances its ability to follow specific instructions and generate appropriate responses, making it a valuable tool for interactive applications.
### Example Usage
Below is an example of how to use the fine-tuned model in your application:
```python
import torch
import random
import numpy as np
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load the fine-tuned model and tokenizer
model = GPT2LMHeadModel.from_pretrained("Autsadin/gpt2_instruct")
tokenizer = GPT2Tokenizer.from_pretrained("Autsadin/gpt2_instruct")
# Define the template for instruction-based prompts
template = '''<s>[INST] <<SYS>>
You are a helpful assistant
<</SYS>>
{instruct}[/INST]'''
# Function to format prompts using the template
def format_entry(prompt):
return template.format(instruct=prompt)
# Define the input prompt
prompt = "What is a dog?"
# Tokenize the input prompt
inputs = tokenizer.encode(format_entry(prompt), return_tensors='pt')
# Generate a response
outputs = model.generate(
inputs,
max_length=256,
num_return_sequences=1,
top_k=50,
top_p=0.95,
temperature=0.8,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
early_stopping=True
)
# Decode and print the generated text
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```
#### Explanation:
#Training Data
The model was fine-tuned using the Alpaca GPT-4 dataset available at the following GitHub repository.
https://github.com/hy5468/TransLLM/tree/main/data/train
Specifically, the alpaca_gpt4_data_en.zip dataset was utilized.
This dataset includes a wide range of instruction-based prompts and responses,
providing a robust foundation for the model's training.
#Training Procedure
The fine-tuning process was carried out with the following hyperparameters:
Learning Rate: 2e-5
Batch Size (Train): 4
Batch Size (Eval): 4
Number of Epochs: 1
Weight Decay: 0.01
#Training Environment
The model was trained using PyTorch and the Hugging Face transformers library. The training was performed on a GPU-enabled environment to accelerate the fine-tuning process.The training script ensures reproducibility by setting a consistent random seed across different components.
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