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. We'll cover the following steps:

1. Installing the required libraries
2. Logging into Hugging Face Hub
3. Loading the necessary libraries and models
4. Formatting the dataset
5. Setting up the training arguments
6. Creating the trainer
7. Training the model
8. Pushing the trained model to Hugging Face Hub

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 define a function to calculate the total number of training steps based on the number of epochs and batch size, and then 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.