CryptGPT: Privacy-Preserving Language Models Using Vigenere Cipher (Part 1)
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Diwank Tomer PRO
diwank
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Post
2145
Just published "CryptGPT: A Simple Approach to Privacy-Preserving Language Models Using the Vigenere Cipher".
https://huggingface.co/blog/diwank/cryptgpt-part1
tl;dr - we pretrained a gpt-2 tokenizer and model from scratch on a dataset encrypted with Vigenere cipher and it performs as well as regular gpt-2. Except in order to use it, you need to know the encryption key.
links:
https://github.com/creatorrr/cryptgpt
diwank/cryptgpt
diwank/cryptgpt-large
https://huggingface.co/blog/diwank/cryptgpt-part1
tl;dr - we pretrained a gpt-2 tokenizer and model from scratch on a dataset encrypted with Vigenere cipher and it performs as well as regular gpt-2. Except in order to use it, you need to know the encryption key.
links:
https://github.com/creatorrr/cryptgpt
diwank/cryptgpt
diwank/cryptgpt-large
Post
1637
Really excited to read about Kolmogorov Arnold Networks as a novel alternatives to Multi Layer Perceptrons.
Excerpt:
> Kolmogorov-Arnold Networks (KANs) are promising alternatives of Multi-Layer Perceptrons (MLPs). KANs have strong mathematical foundations just like MLPs: MLPs are based on the universal approximation theorem, while KANs are based on Kolmogorov-Arnold representation theorem. KANs and MLPs are dual: KANs have activation functions on edges, while MLPs have activation functions on nodes. This simple change makes KANs better (sometimes much better!) than MLPs in terms of both model accuracy and interpretability.
https://github.com/KindXiaoming/pykan
Excerpt:
> Kolmogorov-Arnold Networks (KANs) are promising alternatives of Multi-Layer Perceptrons (MLPs). KANs have strong mathematical foundations just like MLPs: MLPs are based on the universal approximation theorem, while KANs are based on Kolmogorov-Arnold representation theorem. KANs and MLPs are dual: KANs have activation functions on edges, while MLPs have activation functions on nodes. This simple change makes KANs better (sometimes much better!) than MLPs in terms of both model accuracy and interpretability.
https://github.com/KindXiaoming/pykan
models
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diwank/hn-upvote-classifier
Text Classification
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4
diwank/cryptgpt-large
Text Generation
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15
diwank/cryptgpt
Text Generation
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21
diwank/dfe-large-en-2
Sentence Similarity
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2
diwank/dfe-base-en-1
Sentence Similarity
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28
diwank/dfe-base-en
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diwank/dial-flan-silicone
Text2Text Generation
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3
diwank/dial-t0-silicone
Text2Text Generation
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6
diwank/bartner
Text2Text Generation
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5
diwank/silicone-deberta-pair
Text Classification
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19
datasets
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diwank/hn-upvote-data
Viewer
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9.66k
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40
diwank/chessllm-processed
Viewer
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19.9k
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40
diwank/fineweb-edu-2024-10-1M_tokenized-qwen2_1024
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1.03M
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133
diwank/conceptnet-with-descriptions
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3.15M
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43
diwank/llmlingua-compressed-text
Viewer
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222k
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47
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diwank/python-code-execution-output
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3.61k
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45
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diwank/slimorca-corrected-chatml
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359k
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diwank/synthetic-student-profiles
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23.2k
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diwank/goat-deduped
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6.65k
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diwank/expertllama-chatml
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52k
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