Training Progress

Monitor loss and accuracy during the fine-tuning process:

Hyperparameter Tuning

Experiment with different hyperparameters and see their impact:

Learning Rate

1.000e-3

Batch Size

Estimated Loss

1.000

Implementation Example

from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
import torch

# Load pre-trained model
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b")

# Prepare training arguments
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
   per_device_train_batch_size=4,
    learning_rate=2e-5,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir="./logs",
)

# Fine-tune the model
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
    data_collator=DataCollator()
)

Best Practices

Data Preparation

• Clean and validate data
• Balance dataset distribution
• Use proper formatting

Training Strategy

• Implement gradient checkpointing
• Use learning rate scheduling
• Monitor validation metrics

Model Selection

• Choose appropriate model size
• Consider compute resources
• Evaluate model bias

Interactive Example

Sample Training Data

[
  {
    "instruction": "Explain quantum computing",
    "response": "Quantum computing uses quantum...",
    "category": "science"
  },
  {
    "instruction": "Write a poem about spring",
    "response": "Cherry blossoms dance...",
    "category": "creative"
  }
]

Dataset Loading

dataset = load_dataset("json", data_files="train.json")
dataset = dataset.map(format_instruction)

Fine-tuning LLMs

Training Progress

Hyperparameter Tuning

Implementation Example

Best Practices

Data Preparation

Training Strategy

Model Selection

Interactive Example

Sample Training Data

Dataset Loading