metadata

base_model: google/flan-ul2
license: apache-2.0
tags:
  - flan
  - ul2
  - candle
  - quant
pipeline_tag: text2text-generation

flan-ul2: candle quants

Quants of google/flan-ul2 with candle

cargo run --example quantized-t5 --release  -- \
    --model-id pszemraj/candle-flanUL2-quantized \
    --weight-file flan-ul2-q3k.gguf \
    --prompt "Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apples do they have?" \
    --temperature 0

On my laptop (CPU, running in WSL) I get: 45 tokens generated (0.48 token/s)

weights

Below are the weights/file names in this repo:

Weight File Name	Quant Format	Size (GB)
flan-ul2-q2k.gguf	q2k	6.39
flan-ul2-q3k.gguf	q3k	8.36
flan-ul2-q4k.gguf	q4k	10.9
flan-ul2-q6k.gguf	q6k	16

From initial testing:

it appears that q2k is too low precision and produces poor/incoherent output. The q3k and higher are coherent.
Interestingly, there is no noticeable increase in computation time (again, on CPU) when using higher precision quants. I get the same tok/sec for q3k and q6k +/- 0.02

setup

this assumes you already have rust installed

git clone https://github.com/huggingface/candle.git
cd candle
cargo build