The Art of Training Your Own Alpaca-Style ChatGPT: A User-Friendly Guide (Part Two)
Introduction:
In this article, we will delve into the process of finetuning language models like Alpaca and explore how to interact with them. If you have a GPU with sufficient RAM, you can train the model locally. However, for our purposes, we used a cloud platform called Modal, developed by Erik Bernhardsson. Modal allows you to effortlessly run Python code in the cloud. All you need to do is create a wrapper and decorate the function. We provide a sample code snippet to demonstrate the process. Additionally, we discuss important considerations such as selecting the appropriate GPU type and managing memory usage. We also touch upon the available models, focusing on GPT-2, which we finetuned in our experiments. Finally, we compare the performance of the model when using different prompts and highlight the importance of prompt selection.
Full Article: The Art of Training Your Own Alpaca-Style ChatGPT: A User-Friendly Guide (Part Two)
Title: Fine-tuning Language Models Alpaca-style: Techniques and Considerations
Introduction
In this article, we delve into the process of fine-tuning language models Alpaca-style and explore the data and goals associated with it. We also discuss the practical aspects of finetuning a model and engaging in a conversation with it. Whether you have a GPU with ample RAM or prefer a cloud platform like Erik Bernhardsson’s modal, we provide insights to help you get started.
Fine-tuning with Modal
To train the language model, you can either use a local GPU setup or a cloud platform like Modal. The advantage of using Modal is the convenience it offers. Creating a wrapper function allows for an easy setup, as shown in the code snippet below:
import modal
stub = modal.Stub(“finetune_chatbot”)
@stub.function(
image=modal.Image.debian_slim().pip_install_from_requirements(‘requirements.txt’),
shared_volumes={“/finetune”: modal.SharedVolume.from_name(modal_volume_name)},
mounts=[
modal.Mount.from_local_file(“train.py”, remote_path=”/train.py”),
modal.Mount.from_local_dir(“data/modal”, remote_path=”/data/modal”),
] + modal.create_package_mounts([“utils”]),
gpu=modal.gpu.A10G(count=gpu_count),
timeout=60 * 60 * 24,
)
def train():
# training code
# …
if __name__ == “__main__”:
with stub.run():
train.call()
The code above showcases the main requirements for setting up the environment, including the operating system, required Python packages, and mounted volumes. The script also specifies the desired GPU type and sets a timeout limit.
Fine-tuning Considerations
When conducting fine-tuning, there are several parameters to consider:
1. Learning Rate and Epochs: For optimal results, starting finetuning with a learning rate similar to that of the base model is recommended. The smaller Llama models had a learning rate of 3e-5, while Alpaca started with 2e-5.
2. Training Batch Size: Adjusting the `per_device_train_batch_size` and `gradient_accumulation_steps` parameters in modal_run.py can help fit the model into GPU memory. The effective training batch size is the product of these two values. The original Alpaca setup used a batch size of 32 with `per_device_train_batch_size 4`, `per_device_eval_batch_size 4`, and `gradient_accumulation_steps 8`.
3. Training on Multiple GPUs: For training on multiple GPUs, Torch FSDP (Fully Sharded Data Parallel) is required. Personalize the `fsdp_transformer_layer_cls_to_wrap` parameter according to your model’s needs.
Choosing the Right Model
When selecting a model for finetuning, several factors come into play. While Llama is a popular choice, there are numerous text-generating models available on Huggingface. The main consideration is the size of the model, as smaller models are easier and faster to finetune. The GPT-2 model with 124M parameters is widely used due to its efficiency.
Engaging with the Model
Once you have the finetuned model on your computer, you can interact with it using the talk.py script. The Alpaca prompt used in finetuning served as an anchor for the model. For example:
Prompt: “What is the capital of Poland?”
Model response: “The capital of Poland is Warsaw.”
The model demonstrates the ability to respond to instructions accurately. However, it may struggle in some scenarios, such as:
Prompt: “Implore me to brush my teeth.”
Model response: “I would suggest brushing your teeth regularly.”
Prompt: “Write an argument in favor of brushing teeth.”
Model response: “Brushing teeth is a great way to prevent cavities and reduce the risk of developing various diseases associated with poor oral hygiene.”
Prompt: “How much is too much?”
Model response: “Too much is a serious problem and can be caused by factors such as inadequate nutrition, inadequate sleep, and lack of physical activity.”
While the model largely performs well, there are instances where it deviates from the desired response or gets caught in loops, requiring further refinement.
Conclusion
Fine-tuning language models Alpaca-style can be a powerful technique for various natural language processing tasks. Through leveraging frameworks like Modal, developers can easily set up their training environment. By understanding the nuances of parameters and model selection, users can achieve better results. Although the model’s performance may vary, continuous iterations and improvements can lead to more robust conversational abilities in AI language models.
Summary: The Art of Training Your Own Alpaca-Style ChatGPT: A User-Friendly Guide (Part Two)
In this article, we discuss the process of finetuning language models Alpaca-style, including the goals and data involved. We also provide a step-by-step guide on how to train the model using a cloud platform called Modal. We explain the necessary setup, such as creating an online shared volume and setting the learning rate and number of epochs. Additionally, we provide insights on choosing the right model, such as GPT-2, and share an example conversation with the finetuned model. Overall, this article offers valuable information for anyone interested in finetuning language models for specific tasks.
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