Stanford Alpaca vs TinyLlama
Overview
🔥 Trending in Text & Language Models on StackShare
Detailed Comparison
It is a model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations. It behaves qualitatively similarly to OpenAI’s text-davinci-003, while being surprisingly small and easy/cheap to reproduce. | It aims to pre-train a 1.1 billion parameter language model on 3 trillion tokens of text data. It is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. |
Instruction-following model;
Fine-tuned from a 7B LLaMA model on 52K instruction-following data
| 1.1 billion parameter language model;
Trained on 3 trillion tokens of text data;
Uses the same architecture and tokenizer as Llama 2;
Compact and fast |
Statistics | |
GitHub Stars 30.2K | GitHub Stars 8.8K |
GitHub Forks 4.0K | GitHub Forks 572 |
Stacks 0 | Stacks 0 |
Followers 0 | Followers 2 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to Stanford Alpaca, TinyLlama?
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