Summary of Tinyllama: An Open-source Small Language Model, by Peiyuan Zhang et al.
TinyLlama: An Open-Source Small Language Model
by Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, Wei Lu
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary TinyLlama is a compact 1.1 billion-language model that leverages advancements in the Llama 2 architecture and tokenizer, as well as FlashAttention and Lit-GPT contributions from the open-source community. Pretrained on around 1 trillion tokens for approximately 3 epochs, TinyLlama achieves better computational efficiency and remarkable performance in downstream tasks. Despite its relatively small size, it outperforms existing open-source language models with comparable sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TinyLlama is a tiny but mighty language model that can understand and generate text. It was trained on a huge amount of data for a short time, making it fast and efficient. This little model is very good at doing tasks like answering questions or translating languages. And the best part? It’s free to use! |
Keywords
» Artificial intelligence » Gpt » Language model » Llama » Tokenizer
