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BhashaAI/
ViLaH

Name: ViLaH
Rating: 2.5 (10 reviews)

visual-question-answering

Bilingual

damerajee/clean_hin_vqa

gemma

text-generation-inference

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ViLaH

ViLaH (Vision Language Hindi) is a model with 3 billion parameters, fine-tuned from the base-model google/paligemma-3b-pt-224 to handle input images and bilingual (Hindi and English) text sequences for both input and output.

Training Details

Model Configuration: Fine-tuned on a single epoch using a V100 gpu.
Training Duration: Approximately one day.
Evaluation Loss: Achieved an eval loss of 1.6384 at the end of the epoch.
The model is still being train as of right now with better quality dataset
The model's performance may be compromised due to insufficient data and the fact that it was trained for only one epoch.

Dataset

The model was finetuned on only one dataset

damerajee/clean_hin_vqa : This dataset was derived from Lin-Chen/ShareGPT4V and filtered to include only images from the COCO dataset. The original dataset was translated and cleaned to ensure high-quality Hindi visual question answering content.

How to Use

python

!pip install peft trl datasets accelerate bitsandbytes
!pip install transformers --upgrade

To Run the model on a single T4 GPU on Float16

python

from peft import get_peft_model, LoraConfig,prepare_model_for_kbit_training
from transformers import TrainingArguments, Trainer , PaliGemmaForConditionalGeneration , AutoProcessor,BitsAndBytesConfig,AutoTokenizer
from peft import PeftModel, PeftConfig
from datasets import load_dataset
import torch
from datasets import load_dataset

dataset = load_dataset("damerajee/clean_hin_vqa",split='train')
test_example = dataset[10000]
test_image = test_example["image"]
text = test_example['question']

device_index = torch.cuda.current_device()
print("device_index:",device_index)
base_model = PaliGemmaForConditionalGeneration.from_pretrained("BhashaAI/ViLaH",device_map={"": device_index},torch_dtype=torch.float16,low_cpu_mem_usage=True)
processor = AutoProcessor.from_pretrained("BhashaAI/ViLaH")

inputs = processor(text=text, images=test_image, return_tensors="pt").to("cuda")
for k,v in inputs.items():
  print(k,v.shape)

MAX_LENGTH = 200
# Autoregressively generate
# We use greedy decoding here, for more fancy methods see https://huggingface.co/blog/how-to-generate
generated_ids = base_model.generate(**inputs, max_new_tokens=MAX_LENGTH,temperature=0.7,repetition_penalty=2.0,do_sample=True)

# Next we turn each predicted token ID back into a string using the decode method
# We chop of the prompt, which consists of image tokens and our text prompt
image_token_index = base_model.config.image_token_index
num_image_tokens = len(generated_ids[generated_ids==image_token_index])
num_text_tokens = len(processor.tokenizer.encode(text))
num_prompt_tokens = num_image_tokens + num_text_tokens + 2
generated_text = processor.batch_decode(generated_ids[:, num_prompt_tokens:], skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
generated_text

To Run the model on a single T4 GPU in 4Bits

python

from peft import get_peft_model, LoraConfig,prepare_model_for_kbit_training
from transformers import TrainingArguments, Trainer , PaliGemmaForConditionalGeneration , AutoProcessor,BitsAndBytesConfig,AutoTokenizer
from peft import PeftModel, PeftConfig
from datasets import load_dataset
import torch
from datasets import load_dataset

dataset = load_dataset("damerajee/clean_hin_vqa",split='train')
test_example = dataset[10000]
test_image = test_example["image"]
text = test_example['question']

device_index = torch.cuda.current_device()
print("device_index:",device_index)
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
base_model = PaliGemmaForConditionalGeneration.from_pretrained("BhashaAI/ViLaH",device_map={"": device_index},quantization_config=quantization_config,torch_dtype=torch.float16,low_cpu_mem_usage=True)
processor = AutoProcessor.from_pretrained("BhashaAI/ViLaH")

inputs = processor(text=text, images=test_image, return_tensors="pt").to("cuda")
for k,v in inputs.items():
  print(k,v.shape)

MAX_LENGTH = 200
# Autoregressively generate
# We use greedy decoding here, for more fancy methods see https://huggingface.co/blog/how-to-generate
generated_ids = base_model.generate(**inputs, max_new_tokens=MAX_LENGTH,temperature=0.7,repetition_penalty=2.0,do_sample=True)

# Next we turn each predicted token ID back into a string using the decode method
# We chop of the prompt, which consists of image tokens and our text prompt
image_token_index = base_model.config.image_token_index
num_image_tokens = len(generated_ids[generated_ids==image_token_index])
num_text_tokens = len(processor.tokenizer.encode(text))
num_prompt_tokens = num_image_tokens + num_text_tokens + 2
generated_text = processor.batch_decode(generated_ids[:, num_prompt_tokens:], skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
generated_text