Instructions to use team-indain-image-caption/hindi-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use team-indain-image-caption/hindi-image-captioning with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="team-indain-image-caption/hindi-image-captioning")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("team-indain-image-caption/hindi-image-captioning")
model = AutoModelForImageTextToText.from_pretrained("team-indain-image-caption/hindi-image-captioning")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use team-indain-image-caption/hindi-image-captioning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "team-indain-image-caption/hindi-image-captioning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "team-indain-image-caption/hindi-image-captioning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/team-indain-image-caption/hindi-image-captioning

SGLang

How to use team-indain-image-caption/hindi-image-captioning with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "team-indain-image-caption/hindi-image-captioning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "team-indain-image-caption/hindi-image-captioning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "team-indain-image-caption/hindi-image-captioning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "team-indain-image-caption/hindi-image-captioning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use team-indain-image-caption/hindi-image-captioning with Docker Model Runner:
```
docker model run hf.co/team-indain-image-caption/hindi-image-captioning
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Hindi Image Captioning Model

This is an encoder-decoder image captioning model made with VIT encoder and GPT2-Hindi as a decoder. This is a first attempt at using ViT + GPT2-Hindi for image captioning task. We used the Flickr8k Hindi Dataset available on kaggle to train the model.

This model was trained using HuggingFace course community week, organized by Huggingface.

How to use

Here is how to use this model to caption an image of the Flickr8k dataset:

import torch
import requests
from PIL import Image
from transformers import ViTFeatureExtractor, AutoTokenizer, \
                         VisionEncoderDecoderModel

if torch.cuda.is_available():
    device = 'cuda'
else:
    device = 'cpu'

url = 'https://shorturl.at/fvxEQ'
image = Image.open(requests.get(url, stream=True).raw)

encoder_checkpoint = 'google/vit-base-patch16-224'
decoder_checkpoint = 'surajp/gpt2-hindi'
model_checkpoint = 'team-indain-image-caption/hindi-image-captioning'
feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)

#Inference
sample = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]

caption_ids = model.generate(sample, max_length = 50)[0]
caption_text = clean_text(tokenizer.decode(caption_ids))
print(caption_text)

Training data

We used the Flickr8k Hindi Dataset, which is the translated version of the original Flickr8k Dataset, available on Kaggle to train the model.

Training procedure

This model was trained during HuggingFace course community week, organized by Huggingface. The training was done on Kaggle GPU.

Training Parameters

epochs = 8,
batch_size = 8,
Mixed Precision Enabled

Team Members

Downloads last month: 8

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support