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1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
3from PIL import Image
4
5model_id = "qresearch/doubutsu-2b-pt-378"
6model = AutoModelForCausalLM.from_pretrained(
7 model_id,
8 trust_remote_code=True,
9 torch_dtype=torch.float16,
10).to("cuda")
11
12tokenizer = AutoTokenizer.from_pretrained(
13 model_id,
14 use_fast=True,
15)
16
17model.load_adapter("qresearch/doubutsu-2b-lora-378-docci")
18
19image = Image.open("IMAGE")
20
21print(
22 model.answer_question(
23 image, "Describe the image", tokenizer, max_new_tokens=128, temperature=0.1
24 ),
25) .x+=:.
z` ^% .uef^"
.u . . <k .u . :d88E
.u@u .d88B :@8c .u .@8Ned8" .u u .d88B :@8c . `888E
.zWF8888bx ="8888f8888r ud8888. .@^%8888" ud8888. us888u. ="8888f8888r .udR88N 888E .z8k
.888 9888 4888>'88" :888'8888. x88: `)8b. :888'8888. .@88 "8888" 4888>'88" <888'888k 888E~?888L
I888 9888 4888> ' d888 '88%" 8888N=*8888 d888 '88%" 9888 9888 4888> ' 9888 'Y" 888E 888E
I888 9888 4888> 8888.+" %8" R88 8888.+" 9888 9888 4888> 9888 888E 888E
I888 9888 .d888L .+ 8888L @8Wou 9% 8888L 9888 9888 .d888L .+ 9888 888E 888E
`888Nx?888 ^"8888*" '8888c. .+ .888888P` '8888c. .+ 9888 9888 ^"8888*" ?8888u../ 888E 888E
"88" '888 "Y" "88888% ` ^"F "88888% "888*""888" "Y" "8888P' m888N= 888>
88E "YP' "YP' ^Y" ^Y' "P' `Y" 888
98> J88"
'8 @%
` :"