Should I choose huggingface/transformers or rasbt/LLMs-from-scratch for building custom language models?

When considering whether to use huggingface/transformers or rasbt/LLMs-from-scratch for building custom language models, assumptions around adoption and features often surface. However, the nuances between these two options reveal important distinctions.

Overview of Options

1. rasbt/LLMs-from-scratch

   • Stars: 102,907
   • Language: Jupyter Notebook
   • Maintenance: ✅ Actively maintained (last pushed 8 days ago)
   • Open Issues: 2
   • License: No assertion available
   • Description: This repository enables users to implement a ChatGPT-like LLM in PyTorch from scratch, following a structured educational approach. It includes practical code for pretraining and finetuning models.

2. Hugging Face Transformers (General Insights)

   • While direct querying failed to retrieve specific model information from the huggingface/transformers repo, external observations indicate it is a cornerstone in this domain with numerous highly adopted models under its library umbrella. Examples include:
     • sentence-transformers/all-MiniLM-L6-v2: ~258 million downloads, Apache 2.0 license.
     • Transformer's robust environment supports training multiple architectures (including BERT, GPT, etc.) efficiently.

Comparative Analysis

Recommendations

• Choose rasbt/LLMs-from-scratch if you need an educational framework to understand LLMs from the ground up, through code explanations and usage scenarios, particularly if you are focused on custom implementation in PyTorch.
• Choose huggingface/transformers if you want a well-established, heavily adopted framework with a multitude of models readily available for various NLP tasks, enabling ease of integration and deployment.

In conclusion, the decision hinges on whether your priority lies in learning and customization (selecting rasbt/LLMs-from-scratch) or leveraging existing, robust models for practical applications (huggingface/transformers).