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This is the way. I actually mapped out the decision tree for this exact process and more here: https://github.com/NehmeAILabs/llm-sanity-checks https://github.
by 44za12 8mo ago
This is the way. I actually mapped out the decision tree for this exact process and more here:
https://github.com/NehmeAILabs/llm-sanity-checks https://github.com/NehmeAILabs/llm-sanity-checks
- homeonthemtn 8mo agoThat's interesting. Is there any kind of mapping to these respective models somewhere?
- 44za12 8mo agoYes, I included a 'Model Selection Cheat Sheet' in the README (scroll down a bit). I map them by task type: Tiny (<3B): Gemma 3 1B (could try 4B as well), Phi-4-mini (Good for classification). Small (8B-17B): Qwen 3 8B, Llama 4 Scout (Good for RAG/Extraction). Frontier: GPT-5, Llama 4 Maverick, GLM, Kimi Is that what you meant?
- hyuuu 8mo agoat the sake of being obvious, do you have a tiny llm gating this decision and classifying and directing the task to its appropriate solution?
- andai 8mo ago>Before you reach for a frontier model, ask yourself: does this actually need a trillion-parameter model? >Most tasks don't. This repo helps you figure out which ones. About a year ago I was testing Gemini 2.5 Pro and Gemini 2.5 Flash for agentic coding. I found they could both do the same task, but Gemini Pro was way slower and more expensive. This blew my mind because I'd previously been obsessed with "best/smartest model", and suddenly realized what I actually wanted was "fastest/dumbest/cheapest model that can handle my task!"