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 Our mission is to enhance the accessibility and practicality of high-performing language models across a wide range of applications by drastically reducing their computational and energy demands. We believe that our approach has the potential to contribute to more sustainable and inclusive AI development.
 
-For a comprehensive understanding of our research methodology and initial findings, we strongly encourage you to read our paper: [Super Tiny Language Models](https://arxiv.org/abs/2405.14159)
+For a comprehensive understanding of our research methodology and initial findings, we strongly encourage you to read our paper: [Super Tiny Language Models](https://arxiv.org/abs/2405.14159). If you are new to language models we provide some reasources to read up on them in the [STARTER.md](STARTER.md) file.
 
 Please note that this repository is an evolving work in progress, reflecting the ongoing nature of our research. It is subject to frequent updates and improvements as we continue to explore and refine our work on STLMs. We welcome the community's engagement with our work, value your feedback, and appreciate any contributions to this challenging but promising endeavor.
 
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+# Resources for Getting Started with Language Models
+
+## Important Papers
+While academic papers can be difficult to read, don't worry about trying to understand everything. Oftentimes the fine details are not relevant, so try to focus on understanding the main ideas. Here are some papers that are important to understand the development of language models:
+- [the original transformer paper](https://arxiv.org/abs/1706.03762): This paper is an *encoder-decoder* architecture, which is not popular these days. Main things to try to understand from the paper are the introduction of the attention mechanism.
+- [BERT](https://arxiv.org/abs/1810.04805): This paper introduces a 'masked language modelling objective' - reconstructing a partially obscured version of the original text. It is an *encoder* only model
+- [GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf): An example of a *decoder* only model. It uses a causal language modelling objective - predicting the next word in a sentence. More importantly, it shows that just scaling up the model size/data can lead to impressive zero-shot performance on a variety of tasks
+- [GPT-3](https://arxiv.org/abs/2005.14165): This paper introduces a few-shot learning objective - predicting the next word in a sentence given a few examples of the task.
+
+## Blogs
+- [The Illustrated Transformer](http://jalammar.github.io/illustrated-transformer/): A great blog post that explains the transformer architecture in a very visual way.
+- [Illustrated GPT-2](http://jalammar.github.io/illustrated-gpt2/): Another great blog post that explains the GPT-2 architecture in a very visual way.
+- [LLM University](https://cohere.com/llmu): A collection of blog posts that explain various concepts in language models, by Cohere, a prominent AI startup.
+
+## Huggingface
+Huggingface is the de-facto standard for working with and sharing language models. It's great for downstream tasks using pre-trained models, and they provide helpful tools for understanding the concepts behind language models as well as tools for finetuning, a massive collection of datasets, and a large collection of pre-trained models. Here are some resources to get started with Huggingface:
+- [Huggingface Transformers Documentation](https://huggingface.co/transformers/): The official documentation for the Huggingface Transformers library.
+- [Huggingface Datasets Documentation](https://huggingface.co/docs/datasets/): The official documentation for the Huggingface Datasets library.
+- [Huggingface Model Hub](https://huggingface.co/models): The official model hub for Huggingface. You can find a large collection of pre-trained models here.
+
+## Videos
+While we can't vouch for them [Stanford](https://www.youtube.com/playlist?list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4) and [MIT](https://ocw.mit.edu/courses/6-864-advanced-natural-language-processing-fall-2005/) have some free lectures on deep learning and NLP, and their online resources in general are pretty good.
+
+## Repositories
+We highly recommend trying to train a language model from scratch to better understand the concepts. [For example Bobby used this](https://github.com/bobbycxy/gpt-from-scratch) one. Here are some repositories that can help you get started:
+- [MinGPT](https://github.com/karpathy/minGPT) - Very simple PyTorch implementation of GPT-2, also the basis of this repository
+- [LLMs-from-scratch](https://github.com/rasbt/LLMs-from-scratch) - The code from a book of the same name. Should give some understandable implementations of various components across the language modelling pipeline.
+## Key Concepts
+- **Tokenization**: The process of breaking up text into smaller pieces, usually words or subwords. These have integer representations that can be used as input to a model.
+- **Embeddings**: A way to represent words as vectors. These vectors are learned during training and are used as input to the model. Additionally the final outputs of the model also act as embeddings.
+- **Logits**: The raw output of a model before it is converted to probabilities.
+- **Feed Forward Neural Network**: A neural network with (typically) multiple, fully connected layers that each perform a linear transformation followed by a non-linear activation function. These are applied to each token independently.
+- **Attention**: The attention mechanism learns to update the representation of each token based on other tokens. A score is computed that determines how much influence a given token has on another token. Then the "value vectors" of the tokens are combined based on these scores to determine the update
+- **Heads**: Attention heads basically divide the embedding space into multiple subspaces and then apply the attention mechanism to each subspace. This allows the model to learn different types of relationships between tokens.
+- **Transformers**: A model that has interwoven attention and feed forward neural networks. The transformer architecture is composed of multiple layers of these blocks. The transformer architecture is the basis for many modern language models.
+- **Weight Tying**: This is a technique where two layers of a neural network share the same weights -- this forces them to always compute the same function.