bangla-smollm-135m

rnnandi/bangla-smollm-135m 🇧🇩

Model Description

The rnnandi/bangla-smollm-135m is a highly efficient, compact Language Model (LLM) consisting of 135 million parameters, specifically adapted for the Bangla (Bengali) language. It is designed for maximal efficiency, making it suitable for low-latency inference on edge devices, mobile applications, and resource-constrained environments.

The model's evaluation focuses on its ability to handle core Bengali language understanding and reasoning tasks, offering a strong balance between size and performance for the low-resource language domain.

---

Evaluation Results

The model was evaluated using a $0$-shot setting on established Bangla-specific benchmarks. The primary metric is Normalized Accuracy ($\text{acc_norm}$), which adjusts for random chance in multiple-choice questions.

Results Summary (0-shot Normalized Accuracy)

---

Comparative Analysis to Other Compact Models

This section compares the rnnandi/bangla-smollm-135m (135M) against the provided results for Gemma-3-270m (270M) and other popular Bangla-adapted models in the $1\text{B}$ parameter range.

Key Comparative Insights

The rnnandi/bangla-smollm-135m (135M) is highly competitive, maintaining comparable or better performance in complex reasoning tasks (commonsenseqa_bn, bangla_mmlu) than the $\mathbf{270\text{M}}$ parameter Gemma model, highlighting its strong efficiency-to-performance ratio.

---

Intended Use

This model is primarily intended for:

1. Low-Resource Deployment: Applications requiring fast inference and a minimal memory footprint on mobile, edge, or IoT devices.
2. Bangla Text Generation: Generating coherent and contextually relevant text in the Bengali language.
3. Base Model for Transfer Learning: A highly efficient and cost-effective base for fine-tuning on specific, high-value Bengali downstream tasks.

---

How to Use

The model can be loaded directly from the Hugging Face Hub using the transformers library.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "rnnandi/bangla-smollm-135m" 

tokenizer = AutoTokenizer.from_pretrained(model_id)
# Use the appropriate data type for your deployment environment (e.g., torch.float16 or torch.bfloat16)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16) 

# Example: Simple Text Generation Prompt
prompt = "বাংলাদেশ প্রাকৃতিকভাবে সুন্দর" 

inputs = tokenizer(prompt, return_tensors="pt")

# Optional: Move to GPU for faster inference
if torch.cuda.is_available():
    model.to('cuda')
    inputs = {k: v.to('cuda') for k, v in inputs.items()}

outputs = model.generate(
    **inputs, 
    max_new_tokens=64, 
    do_sample=True, 
    temperature=0.7, 
    top_p=0.9,
    eos_token_id=tokenizer.eos_token_id
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

### Citation

Please cite the original work that introduced the bangla_smollm model family if you use this model in your research or application. If a formal paper is not available, please cite the model card directly using the following format:

@misc{bangla_smollm_135m,
  author = {rnnandi},
  title = {rnnandi/bangla-smollm-135m: A Compact 135M Parameter Model for Bangla},
  year = {2025}, % Update year as necessary
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{[https://huggingface.co/rnnandi/bangla-smollm-135m](https://huggingface.co/rnnandi/bangla-smollm-135m)}},
  note = {Model Card}
}