sumitsen/BanglaGptAi1.0

BanglaGptAi1.0

Introduction

BanglaGptAi1.0 is the First release of BanglaGptAi .

• —Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
• —Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
• —Long-context Support up to 128K tokens and can generate up to 8K tokens.
• —Multilingual support for over 29 languages, including Bengali, English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

This repo contains the instruction-tuned 0.5B Qwen2.5 model, which has the following features:

• —Type: Causal Language Models
• —Training Stage: Pretraining & Post-training
• —Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
• —Number of Parameters: 0.49B
• —Number of Paramaters (Non-Embedding): 0.36B
• —Number of Layers: 24
• —Number of Attention Heads (GQA): 14 for Q and 2 for KV
• —Context Length: Full 32,768 tokens and generation 8192 tokens

Requirements

The code of BanglaGptAi has been in the latest Hugging face transformers and we advise you to use the latest version of transformers.

With transformers<4.37.0, you will encounter the following error:

KeyError: 'qwen2'

Quickstart

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sumitsen/BanglaGptAi1.0"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are BanglaGptAi, created by Sumit Sen. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Evaluation & Performance

Detailed evaluation results are reported in this 📑 blog.

For requirements on GPU memory and the respective throughput, see results here.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen2.5,
    title = {BanglaGptAi: A Party of Foundation Models},
    url = {https://},
    author = {Sumit Sen},
    month = {September},
    year = {2026}
}

@article{BanglaGptAi1.0,
      title={BanglaGptAi1.0 Technical Report}, 
      author={Sumit Sen and Crew},
      journal={Technical Report},
      year={2026}
}