llama_cpp_for_radxa_dragon_.../examples
Kawrakow f4d7e54974
SOTA 3-bit quants (#5196)
* iq3_xxs: quantize/dequantize

RMSE seems a bit high-ish at about half-way between q2_K and
q3_K, so need to check more.

* iq3_xxs: CUDA dequantize works

* iq2_xxs: tuning quantization

* iq3_xxs: starting to look better

PPL on wiki.test.raw
LLaMA-v1-7B: 6.4218
LLaMA-v2-7B: 6.3560
Mistral-7B : 6.0717

This is better than Q3_K_XS, with a 5% reduction in quantized model
size.

* iq3_xxs: CUDA dot product

We have
PP-512: 5891 t/s
TG-128: 143.9 t/s

* iq3_xxs: scalar and AVX2 dot products

* iq3_xxs: ARM_NEON and Metal

Metal performance is decent, ARM_NEON is pathetic

* iq3_xxs: slightly better grid points

* Faster iq3_xxs and iq2_xs dot products on CUDA

* iq3_xxs: add some quant mix

* iq3_xxs: fix failing quantization test

Dot product still fails. Is this real?

* iq3_xxs: hopefully fix ROCm

* iq3_xxs: failing tests

This time the dot product accuracy did find an actual bug
in the AVX2 implementation.

* Add IQ3_XXS to test-backend-ops

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-30 15:14:12 +02:00
..
baby-llama
batched
batched-bench
batched.swift
beam-search
benchmark
convert-llama2c-to-ggml
embedding
export-lora
finetune
gguf
imatrix
infill
jeopardy
llama-bench
llama.android
llama.swiftui
llava
lookahead
lookup
main
main-cmake-pkg
parallel
passkey
perplexity
quantize
quantize-stats
save-load-state
server
simple
speculative
sycl
tokenize
train-text-from-scratch
alpaca.sh
base-translate.sh
chat-13B.bat
chat-13B.sh
chat-persistent.sh
chat-vicuna.sh
chat.sh
CMakeLists.txt
gpt4all.sh
json-schema-to-grammar.py
llama.vim
llama2-13b.sh
llama2.sh
llm.vim
make-ggml.py
Miku.sh
pydantic-models-to-grammar-examples.py
pydantic_models_to_grammar.py
reason-act.sh
server-llama2-13B.sh