Instructions to use l3cube-pune/hing-gpt-devanagari with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use l3cube-pune/hing-gpt-devanagari with Transformers:

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

pipe = pipeline("text-generation", model="l3cube-pune/hing-gpt-devanagari")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hing-gpt-devanagari")
model = AutoModelForCausalLM.from_pretrained("l3cube-pune/hing-gpt-devanagari")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use l3cube-pune/hing-gpt-devanagari with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "l3cube-pune/hing-gpt-devanagari"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "l3cube-pune/hing-gpt-devanagari",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/l3cube-pune/hing-gpt-devanagari

SGLang

How to use l3cube-pune/hing-gpt-devanagari 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 "l3cube-pune/hing-gpt-devanagari" \
    --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": "l3cube-pune/hing-gpt-devanagari",
		"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 "l3cube-pune/hing-gpt-devanagari" \
        --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": "l3cube-pune/hing-gpt-devanagari",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use l3cube-pune/hing-gpt-devanagari with Docker Model Runner:
```
docker model run hf.co/l3cube-pune/hing-gpt-devanagari
```

HingGPT-Devanagari

HingGPT-Devanagari is a Hindi-English code-mixed GPT model trained on Devanagari text. It is a GPT2 model trained on L3Cube-HingCorpus.
[dataset link] (https://github.com/l3cube-pune/code-mixed-nlp)

More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.08398)

Other models from HingBERT family:
HingBERT
HingMBERT
HingBERT-Mixed
HingBERT-Mixed-v2
HingRoBERTa
HingRoBERTa-Mixed
HingGPT
HingGPT-Devanagari
HingBERT-LID

@inproceedings{nayak-joshi-2022-l3cube,
    title = "{L}3{C}ube-{H}ing{C}orpus and {H}ing{BERT}: A Code Mixed {H}indi-{E}nglish Dataset and {BERT} Language Models",
    author = "Nayak, Ravindra  and Joshi, Raviraj",
    booktitle = "Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.wildre-1.2",
    pages = "7--12",
}

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Safetensors

Model size

0.1B params

Tensor type

F32

Paper for l3cube-pune/hing-gpt-devanagari

L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models

Paper • 2204.08398 • Published Apr 18, 2022 • 1