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metadata
library_name: transformers
tags: []

Multi-images Multi-audio Multi-turn Malaysian 7B Mistral

WanDB https://wandb.ai/huseinzol05/multimodal-mistral?workspace=user-huseinzol05

how-to 

from modeling_combine import MM_LLMs, MM_LLMs_Config
from transformers import AutoTokenizer, AutoProcessor
from PIL import Image
import librosa
import requests

model = MM_LLMs.from_pretrained(
    'mesolitica/malaysian-mistral-mmmmodal',
    flash_attention = True,
    dtype = torch.bfloat16,
    torch_dtype = torch.bfloat16
)
_ = model.cuda()

image_processor = AutoProcessor.from_pretrained('google/siglip-base-patch16-384')
audio_processor = AutoProcessor.from_pretrained('mesolitica/malaysian-whisper-small')
tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-mistral-mmmmodal')

def prepare_dataset(messages, images: List[str] = None, audio: List[str] = None, sr = 16000):
    
    if images is not None:
        images = [Image.open(f).convert('RGB') for f in images]
        image_output = image_processor(images=images, return_tensors='pt')['pixel_values']
    else:
        image_output = None
        
    if audio is not None:
        audio = [librosa.load(f, sr=sr)[0] for f in audio]
        audio_features = audio_processor(audio, sampling_rate=sr, return_tensors='pt',)['input_features']
    else:
        audio_features = None
    
    prompt = tokenizer.apply_chat_template(messages, tokenize = False)
    outputs = tokenizer(
                    prompt,
                    return_tensors='pt',
                    return_overflowing_tokens=False,
                    return_length=False
    )

    outputs['images'] = image_output
    outputs['audios'] = audio_features
    
    image_token = tokenizer.convert_tokens_to_ids('<image>')
    audio_token = tokenizer.convert_tokens_to_ids('<audio>')
    
    if image_output is not None:
        len_image = len(image_output)
    else:
        len_image = 0
        
    if audio_features is not None:
        len_audio = len(audio_features)
    else:
        len_audio = 0
        
    outputs['image_index'] = torch.tensor([0] * len_image)
    outputs['image_starts'] = torch.tensor([image_token] * (len_image + 1))
    outputs['audio_index'] = torch.tensor([0] * len_audio)
    outputs['audio_starts'] = torch.tensor([audio_token] * (len_audio + 1))
        
    where_is = torch.where((outputs['input_ids'] == image_token) | (outputs['input_ids'] == audio_token))
    ls = []
    for i in range(len(where_is[0])):
        b, k = where_is[0][i], where_is[1][i]
        l = int(outputs['input_ids'][b, k])
        ls.append(l)

    ls = torch.tensor(ls)
    outputs['where_is_b'] = where_is[0]
    outputs['where_is_k'] = where_is[1]
    outputs['ls'] = ls
        
    return outputs

with open('Persian-cat-breed.jpg', 'wb') as fopen:
    fopen.write(requests.get('https://cdn.beautifulnara.net/wp-content/uploads/2017/12/10201620/Persian-cat-breed.jpg').content)

with open('nasi-goreng-1-23.jpg', 'wb') as fopen:
    fopen.write(requests.get('https://www.jocooks.com/wp-content/uploads/2023/09/nasi-goreng-1-23.jpg').content)

with open('test.mp3', 'wb') as fopen:
    fopen.write(requests.get('https://github.com/mesolitica/multimodal-LLM/raw/master/data/test.mp3').content)

messages = [
    {'role': 'user', 'content': '<image> </image> ini gambar apa'},
]
outputs = prepare_dataset(messages, images = ['Persian-cat-breed.jpg'])
if outputs['images'] is not None:
    outputs['images'] = outputs['images'].type(model.dtype)
if outputs['audios'] is not None:
    outputs['audios'] = outputs['audios'].type(model.dtype)
for k in outputs.keys():
    if outputs[k] is not None:
        outputs[k] = outputs[k].cuda()

with torch.no_grad():
    model_inputs = model.prepare_inputs_for_generation(**outputs)
r = model_inputs.pop('input_ids', None)

generate_kwargs = dict(
    model_inputs,
    max_new_tokens=300,
    top_p=0.95,
    top_k=50,
    temperature=0.1,
    do_sample=True,
    num_beams=1,
)

r = model.llm.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))

<s>Imej itu menunjukkan seekor kucing putih yang comel duduk di atas sofa hitam.</s>

messages = [
    {'role': 'user', 'content': '<image> </image> <image> </image> apa kaitan 2 gambar ni'},
]
outputs = prepare_dataset(messages, images = ['Persian-cat-breed.jpg', 'nasi-goreng-1-23.jpg'])
if outputs['images'] is not None:
    outputs['images'] = outputs['images'].type(model.dtype)
if outputs['audios'] is not None:
    outputs['audios'] = outputs['audios'].type(model.dtype)
for k in outputs.keys():
    if outputs[k] is not None:
        outputs[k] = outputs[k].cuda()

with torch.no_grad():
    model_inputs = model.prepare_inputs_for_generation(**outputs)
r = model_inputs.pop('input_ids', None)

generate_kwargs = dict(
    model_inputs,
    max_new_tokens=300,
    top_p=0.95,
    top_k=50,
    temperature=0.1,
    do_sample=True,
    num_beams=1,
)

r = model.llm.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))

<s>Tiada hubungan yang jelas antara gambar 1 (anak kucing putih duduk di atas sofa) dan gambar 2 (foto penutup mangkuk mi telur dengan nasi dan cili). Gambar pertama ialah imej haiwan, manakala gambar kedua ialah imej makanan. Mereka tergolong dalam kategori yang berbeza dan tidak mempunyai hubungan antara satu sama lain.</s>

messages = [
    {'role': 'user', 'content': '<audio> </audio> apa isu audio ni'},
]
outputs = prepare_dataset(messages, audio = [audio])
if outputs['images'] is not None:
    outputs['images'] = outputs['images'].type(model.dtype)
if outputs['audios'] is not None:
    outputs['audios'] = outputs['audios'].type(model.dtype)
for k in outputs.keys():
    if outputs[k] is not None:
        outputs[k] = outputs[k].cuda()

with torch.no_grad():
    model_inputs = model.prepare_inputs_for_generation(**outputs, inference = True)
    
r = model_inputs.pop('input_ids', None)
generate_kwargs = dict(
    model_inputs,
    max_new_tokens=300,
    top_p=0.95,
    top_k=50,
    temperature=0.9,
    do_sample=True,
    num_beams=1,
)

r = model.llm.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))

<s>Isu audio ini berkisar tentang persepsi salah faham dan sikap bakhil berkenaan wang dalam konteks menggalakkan penggunaan e-dompet. Penceramah mencadangkan bahawa orang mungkin keberatan untuk menerima wang kerana tidak melihat manfaat atau nilai menggunakan e-dompet, dan kebimbangan tentang tidak dapat mengakses wang itu jika mereka memerlukannya segera. Penceramah juga menyebut isu ekonomi sistem dan kekurangan sistem yang berkesan di Malaysia. Secara keseluruhannya, isu ini menekankan keperluan untuk pemahaman dan kesedaran yang lebih baik tentang faedah menggunakan e-dompet, serta keperluan untuk pembaharuan sistemik untuk memastikan akses yang saksama kepada wang dan sumber lain.</s>

messages = [
    {'role': 'user', 'content': '<image> </image> <audio> </audio> apa kaitan gambar dan audio ni'},
]
outputs = prepare_dataset(messages, images = [test_image], audio = [audio])
if outputs['images'] is not None:
    outputs['images'] = outputs['images'].type(model.dtype)
if outputs['audios'] is not None:
    outputs['audios'] = outputs['audios'].type(model.dtype)
for k in outputs.keys():
    if outputs[k] is not None:
        outputs[k] = outputs[k].cuda()

with torch.no_grad():
    model_inputs = model.prepare_inputs_for_generation(**outputs, inference = True)
    
r = model_inputs.pop('input_ids', None)
generate_kwargs = dict(
    model_inputs,
    max_new_tokens=300,
    top_p=0.95,
    top_k=50,
    temperature=0.9,
    do_sample=True,
    num_beams=1,
)

r = model.llm.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))

<s>Tidak jelas bagaimana gambar dan audio berkaitan antara satu sama lain. Gambar itu menunjukkan bas pelancongan dengan iklan yang menggalakkan orang ramai menggunakan e-dompet mereka, tetapi ia tidak menyatakan tujuan iklan itu. Audio itu membincangkan idea pembaziran dana sebanyak RM5 juta (kira-kira 1.2 juta USD) ke atas sesuatu projek, tetapi ia tidak menyebut secara langsung bas pelancongan atau e-dompet. Ada kemungkinan bahawa kedua-dua gambar dan audio sedang membincangkan topik yang sama, tetapi lebih banyak konteks diperlukan untuk membuat perkaitan yang pasti.</s>