This model is a continually pre-trained version of the meta-llama/Llama-3.2-3B architecture, fine-tuned on extensive Bangla datasets. The primary goal of the continual pretraining was to enhance the model's ability to generate high-quality Bangla text. By extending the pretraining process specifically on Bangla data, the model has demonstrated superior performance in Bangla language understanding evaluation benchmarks and text generation tasks.

The model is described in the paper TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking. The code for training and evaluation can be found here.

Model Architecture: Llama 3.2 is an auto-regressive language model with optimized transformer architecture.

	Training Data	Params	Input modalities	Output modalities	Context Length	GQA	Shared Embeddings	Token count	Knowledge cutoff
Llama 3.2 (text only)	Hishab curated Bangla text corpus	3B(3.21B)	Monolingual Text(Bangla)	Monolingual Text(Bangla)	4096	Yes	Yes	6B tokens

Supported Languages: Bengali (primary) and English (secondary)

Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.

Model Release Date: October 24, 2024

Status: This is a static model trained on an offline dataset. Future versions may be released to improve model capabilities.

License: We are using a similar license to Llama 3.2. Use of Llama 3.2 is governed by the Llama 3.2 Community License (a custom, commercial license agreement).

How to use

Use with transformers

Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.

Make sure to update your transformers installation via pip install --upgrade transformers.

python

import torch
from transformers import pipeline

model_id = "hishab/titulm-llama-3.2-3b-v1.0"

pipe = pipeline(
    "text-generation", 
    model=model_id, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

pipe("আমাদের দেশের নাম")

Hardware and Software

Training Factors: We used llama-factory training library, Cloud GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on cloud infrastructure.

Training Data

Overview: We have collected a large Bangla raw dataset of text data from a wide variety of sources. Our collected data so far includes a mix of web documents, books, translated text, transliterated text, transcribe text, code-mixed text, conversations, and open-source raw data. The dataset is cleaned and filtered by different filtering criteria to ensure the quality of the data. Our collected data size is roughly around 268 GB. We separated 22GB data from that using a ratio of the data actual data size. Total trained tokens are 6B tokens.

Data sources summary:

Web documents: Extracted, clean, and filtered common crawl data
Books: Extracted, clean, filtered books data
Transcribed text: Used in-house Bangla ASR model to transcribe Bangla audio data
Translation data: We trained an English-Bangla translation LLM model and used it to translate English data to Bangla
Code-mixed data: We trained an English-Bangla code-mixed LLM model and used it to generate code-mixed data
Transliteration data: We trained a Bangla-English transliteration LLM model and used it to generate transliterated data
Synthetic data: We generated synthetic data using a Bangla LLM model
Others: We scrapped some selected website data, used open-source data, and used some other data sources

Benchmarks

In this section, we report the results for titulm-llama-3.2-3b-v1.0 models on standard automatic benchmarks. For all these evaluations, we used lm-evaluation-harness evaluations library.

Evaluation Datasets

We evaluated our pre-trained models on both Bangla and English benchmark datasets. Although the model is trained on Bangla data, its English capability is also evaluated on English benchmark datasets. The evaluation datasets are as follows:

Bangla Benchmark datasets

We evaluated the models on the following datasets:

Bangla MMLU: A private multiple choice question dataset developed by Hishab curated from various sources.
CommonsenseQa Bangla: A Bangla translation of the CommonsenseQA dataset. The dataset was translated using a new method called Expressive Semantic Translation (EST), which combines Google Machine Translation with LLM-based rewriting modifications.
OpenbookQA Bangla: A Bangla translation of the OpenbookQA dataset. The dataset was translated using a new method called Expressive Semantic Translation (EST), which combines Google Machine Translation with LLM-based rewriting modifications.
Piqa Bangla: A Bangla translation of the Piqa dataset. The dataset was translated using a new method called Expressive Semantic Translation (EST), which combines Google Machine Translation with LLM-based rewriting modifications.
BoolQ Bangla: The dataset contains 15,942 examples, with each entry consisting of a triplet: (question, passage, answer). The questions are naturally occurring, generated from unprompted and unconstrained settings. Input passages were sourced from Bangla Wikipedia, Banglapedia, and News Articles, and GPT-4 was used to generate corresponding yes/no questions with answers.

English Benchmark datasets

MMLU: This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge.
CommonseQa: CommonsenseQA is a new multiple-choice question-answering dataset that requires different types of commonsense knowledge to predict the correct answers.
OpenbookQA: OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in.
Piqa: The PIQA dataset focuses on physical commonsense reasoning, challenging AI to handle everyday situations requiring practical knowledge and unconventional solutions. Inspired by instructables.com, it aims to enhance AI's ability to understand and reason about physical interactions.
BoolQ: BoolQ is a question-answer dataset for yes/no questions containing 15942 examples. These questions are naturally occurring. They are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks.

Evaluation Results

Evaluation of Bangla Benchmark datasets

llama-3.2-3b performs better on Bangla MMLU with a 0-shot score of 0.36 and a 5-shot score of 0.38. It also leads in BoolQ BN with a 0-shot score of 0.55 and in OpenBook QA BN with a 5-shot score of 0.32.
hishab/titulm-llama-3.2-3b-v1.0 outperforms in Commonsense QA BN, OpenBook QA BN, and PIQA BN in both 0-shot and 5-shot settings, with the highest score of 0.61 in PIQA BN.

Model	Shots	Bangla MMLU	BoolQ BN	Commonsense QA BN	OpenBook QA BN	PIQA BN
llama-3.2-3b	0-shot	0.36	0.55	0.26	0.31	0.56
	5-shot	0.38	-	0.29	0.32	0.58
hishab/titulm-llama-3.2-3b-v1.0	0-shot	0.36	0.67	0.30	0.35	0.61
	5-shot	0.36	-	0.30	0.35	0.61

Evaluation of English Benchmark datasets

llama-3.2-3b consistently achieves the best scores across all English tasks, with top performances in MMLU, BoolQ, Commonsense QA, OpenBook QA, and PIQA in both 0-shot and 5-shot settings. It reaches a 5-shot score of 0.796 in PIQA.
titulm-llama-3.2-3b-v1.0 shows competitive performance but trails behind llama-3.2-3b in most English benchmarks, particularly in 0-shot settings, though it still performs well in PIQA and Commonsense QA.

Model	Shots	MMLU	BoolQ	Commonsense QA	OpenBook QA	PIQA
llama-3.2-3b	0-shot	0.54	0.73	0.64	0.43	0.77
	5-shot	0.56	0.73	0.67	0.45	0.80
titulm-llama-3.2-3b-v1.0	0-shot	0.47	0.70	0.58	0.39	0.76
	5-shot	0.53	0.70	0.63	0.44	0.78

Instruction Tuned Models

Intended Use

Bangla text generation
Bangla language understanding tasks
Bangla instruction fine-tuning tasks