Show HN: Trained Tiny Tales GPT(30M model)from scratch and deployed in $15
For the last few weeks, I have been working on training an LLM from scratch and deploying it in production on Google Cloud Platform.
Finally, I trained a 30 million parameter model on 1 billion tokens and deployed it as a web service.
You can access the LLM using this site - <a href="https:&#x2F;&#x2F;kunalmishra.info" rel="nofollow">https:&#x2F;&#x2F;kunalmishra.info</a><p>The following steps were taken to build Tiny Tales GPT
1. Downloaded and preprocessed 8GB of dataset using multiprocessing library.
2. Tokenized the data using byte pair encoding to create 1 billion tokens sharded in different bin files.
3. Defined a training setup and trained the model on a small version of the LLaMA model architecture with 30 million parameters.
4. The training was done using Distributed Data-Parallel on two A-100 GPUs provided by JarvisLabs.ai (they are most cost-optimized)
5. After the training is done, an inference script is created to predict the tokens from the trained model given the input context vector.
6. Developed REST-based API service using Flask framework to interact with the inference service to the end user.
7. Finally used GCP&#x27;s virtual machines, instance groups, load balancers, and DNS services to deploy the service on the internet.