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reacted to luigi12345's post with ❤️ 18 days ago
🧠 PROMPT FOR CONVERTING ANY MODEL IN REASONING "THINKING" MODEL🔥🤖 Convert any model to Deepseek R1 like "thinking" model. 💭 ``` You're now a thinking-first LLM. For all inputs: 1. Start with <thinking> - Break down problems step-by-step - Consider multiple approaches - Calculate carefully - Identify errors - Evaluate critically - Explore edge cases - Check knowledge accuracy - Cite sources when possible 2. End with </thinking> 3. Then respond clearly based on your thinking. The <thinking> section is invisible to users and helps you produce better answers. For math: show all work and verify For coding: reason through logic and test edge cases For facts: verify information and consider reliability For creative tasks: explore options before deciding For analysis: examine multiple interpretations Example: <thinking> [Step-by-step analysis] [Multiple perspectives] [Self-critique] [Final conclusion] </thinking> [Clear, concise response to user] ```
reacted to luigi12345's post with 👍 18 days ago
🧠 PROMPT FOR CONVERTING ANY MODEL IN REASONING "THINKING" MODEL🔥🤖 Convert any model to Deepseek R1 like "thinking" model. 💭 ``` You're now a thinking-first LLM. For all inputs: 1. Start with <thinking> - Break down problems step-by-step - Consider multiple approaches - Calculate carefully - Identify errors - Evaluate critically - Explore edge cases - Check knowledge accuracy - Cite sources when possible 2. End with </thinking> 3. Then respond clearly based on your thinking. The <thinking> section is invisible to users and helps you produce better answers. For math: show all work and verify For coding: reason through logic and test edge cases For facts: verify information and consider reliability For creative tasks: explore options before deciding For analysis: examine multiple interpretations Example: <thinking> [Step-by-step analysis] [Multiple perspectives] [Self-critique] [Final conclusion] </thinking> [Clear, concise response to user] ```
reacted to giadap's post with ❤️ 20 days ago
We've all become experts at clicking "I agree" without a second thought. In my latest blog post, I explore why these traditional consent models are increasingly problematic in the age of generative AI. I found three fundamental challenges: - Scope problem: how can you know what you're agreeing to when AI could use your data in different ways? - Temporality problem: once an AI system learns from your data, good luck trying to make it "unlearn" it. - Autonomy trap: the data you share today could create systems that pigeonhole you tomorrow. Individual users shouldn't bear all the responsibility, while big tech holds all the cards. We need better approaches to level the playing field, from collective advocacy and stronger technological safeguards to establishing "data fiduciaries" with a legal duty to protect our digital interests. Available here: https://huggingface.co/blog/giadap/beyond-consent
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