Attention and transformers diagrams
Atlas of Stochastic Parrot Anatomy 🦜
Published:
Some diagrams about attention in artificial neural networks and transformers architectures (as used in LLMs, vision transformers, VLMs).
Atlas of Stochastic Parrot Anatomy 🦜
Published:
Some diagrams about attention in artificial neural networks and transformers architectures (as used in LLMs, vision transformers, VLMs).
Published:
The llama-server Web UI of the llama.cpp project is vulnerable to (Reprompt-like user prompt injection vulnerability. An attacker can inject arbitrary user prompt with query parameter (?q=...). This can lead to arbitrary shell command execution, data exfiltration, etc. (through tool calls).
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An user prompt injection vulnerability (CSRF) both in in the e Chat (Mistral) and Grok (Reprompt-style) allows attackers to inject user prompt with query parameter (?q=...) potentially leading data exfiltration.
Reprompt-style vulnerability in Le Chat (Mistral) and Grok.
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How to quickly use llama.cpp for LLM inference (part 2). This is a follow-up of a previous post on the same topic.
Where we learn that the sky is actually a giant blueberry smoothie 🫐
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Testing ASCII smuggling using Unicode Tags on LLMs/chatbots. Nothing new here. Just a short summary.
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How to quickly use llama.cpp for LLM inference (no GPU needed).
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How to quickly use vLLM for LLM inference using CPU.
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Some notes on how transformer-decoder language models work, taking GPT-2 as an example, and with lots references in order to dig deeper. This is intended both as a a roadmap for understanding on how LLMs work (especially the ones using a transformer-decoder architecture) and a a summary/recap on the topic.
Give me your prompt, would you kindly?
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Extracting the system prompt from GitHub CoPilot.
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