isma

The maintainer of libxml2 put it very well

We're all a little psycho. Now we can all be a little American.

Wait, don't go, it's really simple. It’s a large language model, self-hosted on your own rig. You grab a GGUF file—think distilled neural net weights, quantized to the nth degree, like Q4_K_M with 4-bit precision or Q8_0 with 8-bit integer ops, packed tight with GGML optimizations for minimal memory footprint. Snag it off Hugging Face, maybe a 7B parameter model, 7 billion weights, fits in about 4-6GB of VRAM if you’re lucky. Then you compile llama.cpp—straight C++ inference engine, leverages SIMD instructions, single instruction multiple data, for parallel crunching. Point it at the GGUF, and it’s live, no cloud, no nonsense. Hardware’s key. You need a beefy GPU—say an NVIDIA RTX 4090 with 24GB GDDR6X VRAM, tensor cores screaming at 16-bit float precision, pushing 30 tokens per second on a 13B model. CPU fallback’s doable, Intel i9-13900K with 24 cores, 32 threads, AVX-512 support for vectorized math, but it’ll crawl at 5 tokens per second tops. RAM’s non-negotiable—64GB DDR5 at 5600 MT/s, because context spills into system memory past 8k tokens. Storage? NVMe SSD, Samsung 990 Pro, 2TB, 7450MB/s read, keep those weights streaming. Settings are a playground. Temperature’s a float, 0.65 for tight coherence, 1.8 if you want it spitting chaotic embeddings. Context length—4096 tokens, 4k word fragments, needs 16GB VRAM or it swaps to RAM and stutters. Tokenization’s baked into the GGUF, BPE algo, byte pair encoding, splits text into subword units, 50k vocab size typical. Tuning? LoRA’s your ticket—low-rank adaptation, slap a 16-rank delta on the weight matrix, fine-tune on a 3080 Ti in half a day if you’ve got the dataset. Crazy thought—could you cram it on a Raspberry Pi 5? 8GB LPDDR4X, ARM Cortex-A76, no CUDA, so you’re stuck with CPU inference. Maybe a 1B parameter model, Q2 quantization, 2-bit weights, 500MB footprint. Chugs at 1 token per second if the thermals don’t throttle it to death. Overclock it, liquid cool it, who knows? I’d benchmark it just to see the bus bandwidth choke. Stock’s fine for most, though—13B on a 3090, call it a day. ... Ladies?