Instructions to use shahidul034/text_generation_bangla_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shahidul034/text_generation_bangla_model with Transformers:

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

pipe = pipeline("text-generation", model="shahidul034/text_generation_bangla_model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shahidul034/text_generation_bangla_model")
model = AutoModelForCausalLM.from_pretrained("shahidul034/text_generation_bangla_model")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shahidul034/text_generation_bangla_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shahidul034/text_generation_bangla_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shahidul034/text_generation_bangla_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shahidul034/text_generation_bangla_model

SGLang

How to use shahidul034/text_generation_bangla_model 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 "shahidul034/text_generation_bangla_model" \
    --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": "shahidul034/text_generation_bangla_model",
		"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 "shahidul034/text_generation_bangla_model" \
        --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": "shahidul034/text_generation_bangla_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shahidul034/text_generation_bangla_model with Docker Model Runner:
```
docker model run hf.co/shahidul034/text_generation_bangla_model
```

text_generation_bangla_model / README.md

shahidul034

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	# text_generation_bangla_model
	BanglaCLM dataset:

	- OSCAR: 12.84GB

	- Wikipedia dump: 6.24GB

	- ProthomAlo: 3.92GB

	- Kalerkantho: 3.24GB


	## Model description

	- context size : 128


	## Training and evaluation data
	The BanglaCLM data set is divided into a training set (90%)and a validation set (10%).


	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:

	- Batch size: 32

	- Initial learning rate: 5e-5

	- Number of warmup steps: 10000

	- Weight decay rate: 0.01

	- Tokenization algorithm: BPE

	- Vocabulary size of tokenizer: 50256

	- Total trainable params: 124,439,808

	- Epochs: 40

	- Number of training steps: 40772228

	- training_precision: float32


	### Training results

	perplexity score: 2.86.


	### Framework versions

	- Transformers 4.26.1
	- TensorFlow 2.11.0
	- Datasets 2.10.0
	- Tokenizers 0.13.2

	### Citation
	If you find this model helpful, please cite.
	```
	@INPROCEEDINGS{10303383,
	author={Salim, Md. Shahidul and Murad, Hasan and Das, Dola and Ahmed, Faisal},
	booktitle={2023 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD)},
	title={BanglaGPT: A Generative Pretrained Transformer-Based Model for Bangla Language},
	year={2023},
	volume={},
	number={},
	pages={56-59},
	doi={10.1109/ICICT4SD59951.2023.10303383}}

	```