Artefact2

Which GGUF is right for me? (Opinionated)

Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggerganov/llama.cpp#5962

In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.

llama.cpp feature matrix

See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix

mattiasarro

# Taken from https://johanwind.github.io/2023/03/23/rwkv_details.html. 
# I've added additional comments restructured it a tiny bit, which makes it clearer for me.

import numpy as np
from torch import load as torch_load  # Only for loading the model weights
from tokenizers import Tokenizer

exp = np.exp
layer_norm = lambda x, w, b : (x - np.mean(x)) / np.std(x) * w + b
sigmoid = lambda x : 1/(1 + exp(-x))

zcourts

https://duckduckgo.com/?q=tmux+cheat+sheet&atb=v47-1_x&ia=cheatsheet&iax=1

kevin-smets

Default

Powerlevel10k

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD