Are you ready to dive into the world of AI and enhance your language model’s capabilities? In this guide, we’ll explore how to fine-tune the TinyLlama1.1B model for Retrieval-Augmented Generation (RAG) applications. Join us as we transform our model to generate correct and contextually appropriate responses while minimizing hallucination issues!

Understanding the Problem: Hallucination in Language Models

Imagine a chatty friend who sometimes answers questions with totally irrelevant or incorrect info. That’s what happens with basic language models—they “hallucinate,” generating responses that may not make sense at all. By finetuning the TinyLlama1.1B model, we aim to reduce these missteps and improve accuracy in responding to user queries.

How to Fine-Tune the Model

Let’s break down the steps for using this newly fine-tuned model. Follow these instructions carefully, and you’re bound to get great results!

1. Install Dependencies

First, you need to install the required packages. Open your terminal and run the following command:

bash
pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7

2. Model Inference Code

Next, we’ll need some Python code to implement model inference. Here’s how the setup looks:

python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import pprint

torch.set_default_device('cuda')

# Create model
model = AutoModelForCausalLM.from_pretrained("MuntasirAhmedTinyLlama-1.1B-rag-finetuned-v1.0", 
                                              torch_dtype=torch.float16, 
                                              device_map='auto', 
                                              trust_remote_code=True)

tokenizer = AutoTokenizer.from_pretrained("MuntasirAhmedTinyLlama-1.1B-rag-finetuned-v1.0", 
                                          trust_remote_code=True)

pipe = pipeline(task='text-generation', 
                model=model, 
                tokenizer=tokenizer, 
                max_length=200)

# Set inputs
prompt = "What is a large language model?"
formated_prompt = "system: You are a friendly chatbot who responds to the user's question by looking into context. user: " + prompt

# Generate the answer
result = pipe(formated_prompt)
pprint.pp(result[0]['generated_text'])

Breaking Down the Code: An Analogy

Think of the code like preparing a gourmet meal. First, you gather your ingredients (installing dependencies) to ensure you have everything ready. Then, you follow a precise recipe (the lines of code) that guides you through the steps of mixing and cooking (model inference). Each line contributes to creating a satisfying dish—your chatbot response—by combining user prompts with learned context.

Troubleshooting Tips

While using the TinyLlama1.1B model, you might run into a few hiccups. Here are some troubleshooting ideas to help you resolve common issues:

• Installation Issues: If you encounter issues while installing dependencies, ensure that you have the correct versions of Python and pip. Upgrading pip can often solve these problems.
• CUDA Errors: Make sure your machine supports CUDA if you are setting it to run on a GPU. Otherwise, switch to CPU by changing the default device in your code.
• Output Doesn’t Make Sense: If the generated text seems nonsensical, double-check your fine-tuning dataset. Including both meaningful questions and hallucination cases can improve accuracy.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

With your fine-tuned TinyLlama1.1B model, you’re now set to tackle user queries more effectively and with less chance of miscommunication. In artificial intelligence, every advancement pushes the boundaries of what’s possible, leading to more effective 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.