Fine Tuning TinyLlama for Text Generation with TRL

In this tutorial, we'll walk through the process of training a language model using the TinyLlama model and the Transformers library.

1. Installing the Required Libraries

We'll start by installing the necessary libraries using pip:

!pip install -q datasets accelerate evaluate trl accelerate

2. Logging into Hugging Face Hub

Next, we'll log into the Hugging Face Hub to access the required models and datasets:

from huggingface_hub import notebook_login

notebook_login()

3. Loading the Necessary Libraries and Models

We'll import the required libraries and load the TinyLlama model and tokenizer:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

4. Formatting the Dataset

We'll define a function to format the prompts in the dataset and load the dataset:

def format_prompts(examples):
    """
    Define the format for your dataset
    This function should return a dictionary with a 'text' key containing the formatted prompts.
    """
    pass

from datasets import load_dataset

dataset = load_dataset("your_dataset_name", split="train")
dataset = dataset.map(format_prompts, batched=True)

dataset['text'][2] # Check to see if the fields were formatted correctly

5. Setting Up the Training Arguments

We'll set up the training arguments:

from transformers import TrainingArguments

args = TrainingArguments(
    output_dir="your_output_dir",
    num_train_epochs=4, # replace this, depending on your dataset
    per_device_train_batch_size=16,
    learning_rate=1e-4,
    save_steps=100000, # stupid number to save storage
    optim="sgd",
    optim_target_modules=["attn", "mlp"]
)

6. Creating the Trainer

We'll create an instance of the SFTTrainer from the trl library:

from trl import SFTTrainer

trainer = SFTTrainer(
    model=model,
    args=args,
    train_dataset=dataset,
    dataset_text_field='text',
    max_seq_length=1024,
)

7. Training the Model

Finally, we'll start the training process:

trainer.train()

8. Pushing the Trained Model to Hugging Face Hub

After the training is complete, you can push the trained model to the Hugging Face Hub using the following command:

trainer.push_to_hub()

This will upload the model to your Hugging Face Hub account, making it available for future use or sharing.

That's it! You now have a trained language model using the TinyLlama model. Feel free to modify the code or experiment with different configurations as needed.