Omar Hosney

LLM Fine-Tuning Cheat Sheet 🚀

Quantization 🔢

Concept: Convert higher precision to lower precision, reducing model size. 📉
Types: Post-training (PTQ) and Quantization-aware training (QAT). 🔄
Benefits: Faster inference, lower memory usage, and energy efficiency. ⚡
Trade-off: Slight accuracy loss vs. significant performance gain. ⚖️
Common formats: INT8, FP16, and recently, even 1-bit quantization. 🧮

LoRA (Low-Rank Adaptation) 🔬

Purpose: Efficient fine-tuning by updating a small number of parameters. 🎯
Technique: Decomposes weight updates into low-rank matrices. 🧩
Advantage: Significantly reduces memory and computational requirements. 💾
Rank: A hyperparameter controlling the trade-off between efficiency and capacity. 🔢
Application: Useful for adapting large models to specific tasks or domains. 🔧

QLoRA 🔬🔢

Concept: Combines quantization with LoRA for even more efficient fine-tuning. 🔗
Process: Quantizes the base model, applies LoRA on top of quantized weights. 🔄
Benefit: Enables fine-tuning of very large models on consumer hardware. 💻
Performance: Often achieves results comparable to full fine-tuning. 📊
Use case: Ideal for resource-constrained environments or large-scale deployments. 🌐

Fine-Tuning Process 🔧

Data preparation: Clean, format, and augment your dataset. 🧹
Model selection: Choose a pre-trained model as your starting point. 🎭
Hyperparameter tuning: Adjust learning rate, batch size, and epochs. 🎛️
Training: Use techniques like gradient accumulation and mixed precision. 🏋️‍♂️
Evaluation: Assess performance on validation set and iterate as needed. 📏

Prompt Engineering 💬

Importance: Crucial for guiding model behavior and improving outputs. 🎯
Techniques: Few-shot learning, chain-of-thought, and zero-shot prompting. 🧠
Best practices: Be specific, provide context, and use consistent formatting. 📝
Iteration: Continuously refine prompts based on model responses. 🔄
Tools: Experiment with prompt optimization frameworks and libraries. 🛠️

Evaluation Metrics 📊

Perplexity: Measures how well the model predicts a sample. Lower is better. 📉
BLEU score: Evaluates generated text against reference translations. 🔤
ROUGE: Assesses the quality of generated summaries. 📚
Human evaluation: Essential for assessing subjective quality and safety. 👥
Task-specific metrics: Use metrics relevant to your specific use case. 🎯

Ethical Considerations 🤔

Bias mitigation: Address and reduce biases in training data and model outputs. ⚖️
Privacy: Ensure compliance with data protection regulations like GDPR. 🔒
Transparency: Document model capabilities, limitations, and potential risks. 📜
Safety: Implement content filtering and output moderation. 🛡️
Accountability: Establish clear guidelines for model use and deployment. 📋

Tools and Frameworks 🛠️

Hugging Face: Comprehensive library for NLP tasks and model fine-tuning. 🤗
PyTorch: Popular deep learning framework with extensive LLM support. 🔥
TensorFlow: Google's ML framework with TF-Hub for pre-trained models. 🧠
ONNX: Open format to represent machine learning models. 🌐
MLflow: Platform for the machine learning lifecycle, including experiment tracking. 📊

LLM Fine-Tuning using Gradient Package 🚀

Gradient Package: Setup 🛠️

Installation: Use pip to install the Gradient package. 📦
Authentication: Set up workspace ID and access token. 🔑
Model Selection: Choose a base model for fine-tuning. 🎭


# Install Gradient
pip install gradient

# Set up environment variables
import os
os.environ["GRADIENT_WORKSPACE_ID"] = "your_workspace_id"
os.environ["GRADIENT_ACCESS_TOKEN"] = "your_access_token"

# Initialize Gradient
from gradient import Gradient
gradient = Gradient()

# Get base model
base_model = gradient.get_base_model("base_model_slug")

Gradient Package: Data Preparation 📊

Format: Prepare data in the required format for fine-tuning. 📝
Sample Data: Create a list of dictionaries with instructions and responses. 📚
Customization: Tailor the data to your specific use case. 🎨


# Prepare sample data
samples = [
    {
        "instruction": "Who is Krish?",
        "response": "Krish is a popular mentor and YouTuber who uploads videos on data science and AI."
    },
    {
        "instruction": "What do you know about Krish?",
        "response": "Krish is a content creator specializing in data science. His YouTube channel provides educational content on AI and machine learning."
    }
]

Gradient Package: Fine-Tuning 🔧

Model Adapter: Create a new model adapter for fine-tuning. 🔄
Fine-Tuning Process: Use the fine_tune method with prepared samples. 🏋️‍♂️
Iteration: Fine-tune for multiple epochs as needed. 🔁
Evaluation: Test the fine-tuned model with new queries. 📊


# Create model adapter
new_model = base_model.create_model_adapter("my_fine_tuned_model")

# Fine-tune the model
num_epochs = 3
for epoch in range(num_epochs):
    new_model.fine_tune(samples=samples)

# Test the fine-tuned model
query = "Tell me about Krish"
response = new_model.complete(query).generated_output
print(response)