LLMs-from-scratch
kafkaflow
| LLMs-from-scratch | kafkaflow | |
|---|---|---|
| 9 | 3 | |
| 16,129 | 580 | |
| - | 1.7% | |
| 9.6 | 8.6 | |
| 1 day ago | 3 days ago | |
| Jupyter Notebook | C# | |
| GNU General Public License v3.0 or later | MIT License |
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LLMs-from-scratch
- Finetune a GPT Model for Spam Detection on Your Laptop in Just 5 Minutes
- Insights from Finetuning LLMs for Classification Tasks
-
Ask HN: Textbook Regarding LLMs
https://www.manning.com/books/build-a-large-language-model-f...
- Comparing 5 ways to implement Multihead Attention in PyTorch
- FLaNK Stack 29 Jan 2024
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Implementing a ChatGPT-like LLM from scratch, step by step
The attention mechanism we implement in this book* is specific to LLMs in terms of the text inputs, but it's fundamentally the same attention mechanism that is used in vision transformers. The only difference is that in LLMs, you turn text into tokens, and convert these tokens into vector embeddings that go into an LLM. In vision transformers, instead of regarding images as tokens, you use an image patch as a token and turn those into vector embeddings (a bit hard to explain without visuals here). In both text or vision context, it's the same attention mechanism, and it both cases it receives vector embeddings.
(*Chapter 3, already submitted last week and should be online in the MEAP soon, in the meantime the code along with the notes is also available here: https://github.com/rasbt/LLMs-from-scratch/blob/main/ch03/01...)
kafkaflow
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FLaNK-HuggingFace-BLOOM-LLM - https://huggingface.co/bigscience/bloom into NiFi
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dbmate - :rocket: A lightweight, framework-agnostic database migration tool.