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)
| Benchmark | Task | Metric | Score ($\text{acc_norm}$) | Standard Error |
|---|---|---|---|---|
| piqa_bn | Physical Interaction Q/A | $$\text{acc_norm}$$ | 0.545 | $$\pm 0.012$$ |
| openbookqa_bn | Factual Knowledge Q/A | $$\text{acc_norm}$$ | 0.320 | $$\pm 0.021$$ |
| commonsenseqa_bn | Common Sense Reasoning | $$\text{acc_norm}$$ | 0.256 | $$\pm 0.013$$ |
| bangla_mmlu | Knowledge & Reasoning | $$\text{acc_norm}$$ | 0.237 | $$\pm 0.004$$ |
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.
| Model | Size | PIQA BN ($\text{acc_norm}$) | OpenBook QA BN ($\text{acc_norm}$) | Commonsense QA BN ($\text{acc_norm}$) | Bangla MMLU ($\text{acc_norm}$) |
|---|---|---|---|---|---|
| rnnandi/bangla-smollm-135m | 135M | 0.545 | 0.320 | 0.256 | 0.237 |
| Gemma-3-270m | 270M | 0.547 | 0.336 | 0.249 | 0.234 |
| Llama-3.2-1b | 1B | 0.530 | 0.320 | 0.220 | 0.290 |
| titulm-llama-3.2-1b-v2.0 | 1B | 0.580 | 0.320 | 0.260 | 0.250 |
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:
- Low-Resource Deployment: Applications requiring fast inference and a minimal memory footprint on mobile, edge, or IoT devices.
- Bangla Text Generation: Generating coherent and contextually relevant text in the Bengali language.
- 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}
}