Welcome to the world of AI! Today, we are diving deeper into the intricate process of fine-tuning a model tailored specifically for Java test scripts using the Codellama architecture. Our example will highlight a model that has been trained with over 400 Java test scripts leveraging the Cucumber and Selenium frameworks. Let’s unravel this together!

Understanding the Model Overview

The core of our project revolves around the Codellama-7b-hf base model, further refined with a dataset that can be located at shyam-incedoincqa-finetune-dataset. This dataset consists of numerous Java test scripts, allowing our model to learn the nuances of testing within that particular programming paradigm.

Training Parameters

Before we proceed with the coding part, let’s review some essential training parameters:

• Number of Training Epochs: 25
• Batch Size: 2
• Learning Rate: 2e-4
• Weight Decay: 0.001
• Gradient Checkpointing: Enabled
• Optimizer: paged_adamw_32bit
• FP16: Enabled
• Model Saving Strategy: Epoch based
• Seed: 42 (for reproducibility)

Getting Inferences from the Model

The heart of our tutorial involves the code that allows you to extract inferences from the fine-tuned model:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

hf_model_repo = "shyam-incedoinccodellama-7b-hf-peft-qlora-finetuned-qa"

# Get the tokenizer
tokenizer = AutoTokenizer.from_pretrained(hf_model_repo)

# Load the model
model = AutoModelForCausalLM.from_pretrained(
    hf_model_repo, 
    load_in_4bit=True, 
    torch_dtype=torch.float16, 
    device_map='auto'
)

# Load dataset from the hub
hf_data_repo = "shyam-incedoincqa-finetune-dataset"
train_dataset = load_dataset(hf_data_repo, split='train')
valid_dataset = load_dataset(hf_data_repo, split='validation')

# Load the sample
sample = valid_dataset[randrange(len(valid_dataset))]['text']
groundtruth = sample.split("### Output:\n")[1]
prompt = sample.split("### Output:\n")[0] + "### Output:\n"

# Generate response
input_ids = tokenizer(prompt, return_tensors='pt', truncation=True).input_ids.cuda()
outputs = model.generate(input_ids=input_ids, max_new_tokens=1024, do_sample=True, top_p=0.9, temperature=0.6)

# Print the result
print(f"Generated response:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]}")
print(f"Ground Truth:\n{groundtruth}") 

Explaining the Code: An Analogy

Think of your model as a chef preparing a very special dish (the inferences). To make the dish just right, the chef needs various ingredients (the training data) and a set of precise cooking instructions (the code). Below is the breakdown of our recipe:

• Importing Necessary Libraries: Just as our chef needs certain tools (like knives and pots), we import libraries to give us the functionalities needed for our model.
• Setting Up the Model: Selecting the right chef (the pre-trained model) and ensuring they prepare our dish in the right way based on our specific needs.
• Loading Ingredients: We gather the training dataset, which contains all the crucial recipes (test scripts) we need.
• Preparing the Dish: The prompt acts as the ordered ingredients which set the stage for generating our dish (the model’s output).
• Cooking: Using the model to generate a response is akin to our chef fervently mixing and cooking the ingredients.
• Serving: Finally, we print out the generated response to see how well our dish came out compared to the ground truth.

Troubleshooting Tips

• Model Not Loading: Ensure that you have the correct path specified for your model and dataset. Check your internet connection if loading from remote.
• CUDA runtime errors: Confirm that your PyTorch version is installed with CUDA support, and that your graphics card is compatible.
• Version Conflicts: Verify that all libraries are updated and compatible with each other to avoid conflicts.
• Output Not as Expected: Review the model’s training parameters; tuning these parameters can significantly affect performance.

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Conclusion

By following this guide, you’ll be able to fine-tune a model for Java-based testing scripts, leveraging the power of Codellama. As AI continues to evolve, understanding such methodologies becomes crucial for developing sophisticated solutions.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.