Model Card for Model ID
A fine-tuned Llama-3.1-8B-Instruct model specialized for converting regional/dialectal Bangla speech (Chittagonian, Sylheti, Barishali, etc.) into clean, standard written Bangla.
It takes noisy or dialect-heavy Bangla transcriptions as input and reliably outputs grammatically correct, formal Standard Bangla while preserving the original meaning — perfect for post-processing ASR outputs from regional Bangladeshi speech.
Model Details
This model is a fine-tuned version of the LLaMA 3.1 8B Instruct architecture, optimized specifically for Bangladeshi regional dialect to Standard Bangla text normalization. The training was carried out using the Unsloth framework, which allows efficient LoRA fine-tuning on low-memory hardware such as the T4 GPU available on Kaggle. The model was trained on a dataset consisting of regional Bangla expressions paired with their correct Standard Bangla equivalents. Through careful preprocessing, filtering, and normalization of the dataset, the model was able to learn consistent linguistic patterns across dialects. The training process included a standard train–validation split and continual monitoring of the validation loss to select the best-performing checkpoint. The resulting model is compact, memory-efficient, and highly effective at handling dialect variations while maintaining fluency and correctness in Standard Bangla.
Model Description
This model converts Bangladeshi regional dialect text into Standard Bangla with high accuracy, making it ideal for ASR post-processing, dialect normalization, and text standardization tasks. It is capable of transforming colloquial, informal, or region-specific Bangla phrases into clear, grammatically correct Standard Bangla, even when the input contains fragmented or ambiguous forms. The training process used an instruction-tuned approach in which each sample included a user prompt and the expected normalized response, allowing the model to learn the structure of conversational corrections and editing tasks. By employing QLoRA during training, the model maintains strong performance while requiring significantly less GPU memory, enabling smooth experimentation on the Kaggle free tier environment. The final model generalizes well to unseen dialect inputs and integrates easily into existing NLP pipelines, model hosting solutions, and competition submission workflows
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