Project: Evaluating a Fine-Tuned Model

Duration: 5 min

This module focuses on evaluating fine-tuned language models using various techniques such as LoRA, QLoRA, PEFT, Instruction Tuning, RLHF, and DPO. Understanding how to evaluate these models is crucial for ensuring their performance and reliability in real-world applications.

Evaluating with LoRA and QLoRA

Low-Rank Adaptation (LoRA) and its quantized version, QLoRA, are techniques used to fine-tune large language models efficiently. Evaluating models fine-tuned with these methods involves assessing their performance on specific tasks and comparing them against baseline models.

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned LoRA model and tokenizer
model_name = 'fine-tuned-lora-model'
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define a sample input
input_text = 'Translate English to French: The house is wonderful.'
input_ids = tokenizer(input_text, return_tensors='pt').input_ids

# Generate output
output = model.generate(input_ids, max_length=50)
decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)

print(decoded_output)

Try it in Google Colab:

La maison est merveilleuse.

Evaluating with PEFT and Instruction Tuning

Parameter-Efficient Fine-Tuning (PEFT) and Instruction Tuning are methods to fine-tune models with minimal parameter updates. Evaluating these models requires running them on benchmark datasets and analyzing metrics like accuracy, BLEU score, or perplexity.

import transformers
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the fine-tuned PEFT model and tokenizer
model_name = 'fine-tuned-peft-model'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define a sample input
input_text = 'Summarize: The quick brown fox jumps over the lazy dog.'
input_ids = tokenizer(input_text, return_tensors='pt').input_ids

# Generate summary
output = model.generate(input_ids, max_length=30)
decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)

print(decoded_output)

💡 Tip: Ensure that the evaluation dataset is representative of the tasks the model will perform in production to get accurate performance metrics.