Instructions to use saiful9379/Bangla_GPT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saiful9379/Bangla_GPT2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="saiful9379/Bangla_GPT2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("saiful9379/Bangla_GPT2") model = AutoModelForCausalLM.from_pretrained("saiful9379/Bangla_GPT2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use saiful9379/Bangla_GPT2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "saiful9379/Bangla_GPT2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "saiful9379/Bangla_GPT2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/saiful9379/Bangla_GPT2
- SGLang
How to use saiful9379/Bangla_GPT2 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 "saiful9379/Bangla_GPT2" \ --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": "saiful9379/Bangla_GPT2", "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 "saiful9379/Bangla_GPT2" \ --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": "saiful9379/Bangla_GPT2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use saiful9379/Bangla_GPT2 with Docker Model Runner:
docker model run hf.co/saiful9379/Bangla_GPT2
Bangla GPT2 model was trained using the Bangla Newspaper dataset. Here we used prothom alo 250mb data for GPT2 model training and also vocab size 50k.
Github link : https://github.com/saiful9379/Bangla_GPT2
from transformers import TFGPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("saiful9379/Bangla_GPT2")
model = TFGPT2LMHeadModel.from_pretrained("saiful9379/Bangla_GPT2")
text = "বহুল আলোচিত দশম জাতীয় সংসদ"
input_ids = tokenizer.encode(text, return_tensors='tf')
print(input_ids)
output = model.generate(
input_ids,
max_length=175,
num_beams=10,
temperature=0.7,
no_repeat_ngram_size=2,
num_return_sequences=5
)
predicted_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(predicted_text)
Here is the basic configuration of Bangla GPT2 Model,
vocab_size = 50000
block_size = 200
learning_rate=3e-5
num_epoch = 100
batch_size = 12
buffer_size = 1000
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