frontrunbitcoin | Nostr Profile

frontrunbitcoin 16h

Tick tock Mr. Wick tick tock 🕰️⛓️

frontrunbitcoin 1d

500 sats for free #plebchain #stackchain Make your first transaction of ฿1,000 / ₮1 or more, and you can earn ฿500 cashback! Enjoy a seamless payment experience: https://links.speed.app/referral?referral_code=ZHU8NJ

#plebchain #stackchain

frontrunbitcoin 3d

🧠 LLMs for the Regarded (A Friendly Guide for the Rest of Us Who Like to Know What the Hell Is Going On) So you downloaded some local LLM because you were tired of paying OpenAI a subscription and wanted to feel like a cyber wizard. Then you ran a script, your GPU started screaming, your RAM lit up like a Christmas tree, and you thought: What even is happening right now? Good. You’re asking the right question. Let’s break it down. ⸻ Step 1: You Don’t Talk to a Model — You Talk to Math When you “run” a model, you’re doing inference — not training, not magic, just prediction. The model takes your words, chops them into weird little text chunks called tokens, turns those into numbers, and guesses the next token. Then it adds that token to your sentence, and guesses again. One. Token. At. A. Time. Every word you read? A machine playing autocomplete — just with a PhD in statistics and a caffeine addiction. ⸻ Step 2: Tokens Are Not Words People love to say “the model reads your words.” Lies. It reads tokens, which are like slices of text — sometimes whole words, sometimes parts of them, sometimes just punctuation. Example: • “Hello” might be 1 token. • “Internationalization” might be 8. • “😂” is 1 token and somehow smarter than us all. Models can only “see” a certain number of tokens at once — called the context window. More context = more memory = slower model = louder GPU fan. ⸻ Step 3: The Brains Are Just Numbers Inside the model live weights — billions of tiny numbers that store what it “learned” about language. They’re not facts; they’re patterns. Think of them as knobs that have been adjusted so the model gets better at guessing what comes next. So when you run a model, you’re loading all those weights into memory. If your GPU doesn’t have enough VRAM, it cries. ⸻ Step 4: Transformers — The Skeleton Crew Underneath everything is the Transformer architecture, a stack of repeating layers that pass your text through like a bureaucratic office. Each layer has two main jobs: 1. Self-attention — “Which previous words actually matter right now?” 2. Feed-forward network — “Okay, let’s turn that realization into math.” Repeat that 30-100 times, and the model eventually says something halfway coherent about cats. ⸻ Step 5: VRAM, the Real Boss All those weights and running calculations have to live somewhere — your GPU’s memory. A rule of thumb: • A 7-billion-parameter model in full quality takes around 14 GB. • You can shrink it using quantization (compressing precision). • 8-bit ≈ 7 GB • 4-bit ≈ 3.5 GB • 2-bit = your model just became a potato There’s also a KV cache — a memory bank of the conversation so the model doesn’t forget what you said five seconds ago. It grows as your chat does, like a goldfish that refuses to die. ⸻ Step 6: Quantization — The Art of Making It Fit Quantization is like telling the model: “You don’t need 32-bit precision to tell me a joke about frogs. Relax.” By rounding the math, you save memory and speed up inference. The tradeoff? Occasionally, it forgets how to do math or hallucinates that the French Revolution happened in 1984. 4-bit quantization is the sweet spot: good enough for most tasks, small enough to fit on consumer GPUs, and only mildly deranged. ⸻ Step 7: Generation — The Token Factory Once the model’s loaded, it plays the same game over and over: current text → predict next token → add it → repeat You can tell it how to “choose” tokens: • Greedy = always pick the most likely. Boring robot. • Temperature = more chaos, more fun. • Top-p or top-k = limit choices to the most probable few. Every new token goes back through the entire model stack. Every. Single. Time. That’s why long outputs are slow — your GPU is basically reenacting Groundhog Day in silicon. ⸻ Step 8: Why Bother Running Locally? Because you like control. And privacy. No network delay, no token fees, no corporate overlords reading your prompts about anime economics. Running locally means: • You control the temperature, top-p, and chat template. • You can tinker, automate, or even fine-tune your own model. • You’ll learn why your GPU gets hotter than the sun. ⸻ Step 9: The “Gotchas” Nobody Tells You • Out of memory? Quantize or shrink context. • Gibberish? You’re using a base model without a chat template. • Slow as molasses? You’re offloading to CPU. Don’t. • Untrustworthy file? Don’t run random .bin weights from strangers. Use safetensors. ⸻ Step 10: The Big Picture Running an LLM isn’t mysterious. It’s just this loop: Text → Tokens → Numbers → Math → Probabilities → New Token → Repeat That’s it. The whole grand AI illusion boils down to predicting what comes next — faster, smarter, and occasionally in Latin. ⸻ TL;DR: The Gospel of Local LLMs • They’re math, not magic. • VRAM is your god. • Quantization is your religion. • FlashAttention is your prayer. • Chat templates are your commandments. • And temperature = chaos. ⸻ You now understand local LLMs better than most YouTubers explaining them. Next time someone says “just run this script,” smile and ask: “Cool, but what precision are those weights, and how’s your KV cache quantized?” Watch their soul leave their body.

frontrunbitcoin 5d

#trumpdollar