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Model: hishab/titulm-llama-3.2-3b-v2.0 Source: Original Platform
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---
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language:
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- bn
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library_name: transformers
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license: llama3.2
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pipeline_tag: text-generation
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tags:
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- hishab
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- titulm
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- pytorch
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- llama
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- llama-3
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- llama-factory
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---
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## Model Information
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This model is a continually pretrained version of the [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) architecture with extended about 42K Bangla tokens, fine-tuned on extensive Bangla datasets. The primary goal of the continual pretraining with token extending 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.
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**Model Architecture:** Llama 3.2 is an auto-regressive language model with optimized transformer architecture.
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| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
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| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
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| Llama 3.2 (text only) | Hishab curated Bangla text corpus | 3B(3.21B) | Monolingual Text(Bangla) | Monolingual Text(Bangla) | 4096 | Yes | Yes | 37B tokens | |
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**Supported Languages:** Bengali (primary) and English (secondary)
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**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
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**Model Release Date:** October 24, 2024
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**Status:** This is a static model trained on an offline dataset. Future versions may be released to improve model capabilities.
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**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](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
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Paper: [TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking](https://huggingface.co/papers/2502.11187)
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<br>
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## How to use
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- Use with transformers
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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.
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Make sure to update your transformers installation via pip install --upgrade transformers.
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```python
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import torch
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from transformers import pipeline
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model_id = "hishab/titulm-llama-3.2-3b-v2.0"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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pipe("আমাদের দেশের নাম")
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```
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## Hardware and Software
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**Training Factors:** We used [llama-factory](https://github.com/hiyouga/LLaMA-Factory) training library, Cloud GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on cloud infrastructure.
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## Training Data
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**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 sources 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. The total trained tokens are 37B tokens.
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Data sources summary:
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- Web documents: Extracted, clean, and filtered common crawl data
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- Books: Extracted, clean, and filtered book data
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- Transcribed text: Used in-house Bangla ASR model to transcribe Bangla audio data
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- Translation data: We trained an English-Bangla translation LLM model and used it to translate English data to Bangla
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- Code-mixed data: We trained an English-Bangla code-mixed LLM model and used it to generate code-mixed data
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- Transliteration data: We trained a Bangla-English transliteration LLM model and used it to generate transliterated data
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- Synthetic data: We generated synthetic data using a Bangla LLM model
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- Others: We scraped some selected website data, used open-source data, and used some other data sources
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## Token Extending
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We trained a separate Bangla tokenizer using [Tiktoken](https://github.com/openai/tiktoken) library on 48 GB Bangla datasets (sampled from main pretraining data) with a vocabulary size of 48k and separated 42k tokens for adding with the pretrained model. We extended the model's vocabulary with these tokens and continued the pretraining process on Bangla data. The token-extending process was done to enhance the model's ability to generate high-quality Bangla text. Our updated vocab size is 170K whereas the original llama-3.2 vocab size is 128k.
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## Benchmarks \- Bangla Text
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In this section, we report the results for __titulm-llama-3.2-1b-v2.0__ models on standard automatic benchmarks. For all these evaluations, we used [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) evaluations library.
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### Evaluation Datasets
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We evaluated our pretrained models on both Bangla and English benchmark datasets. Although the model is trained on Bangla data, it's English capability is also evaluated on English benchmark datasets. The evaluation datasets are as follows:
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#### Bangla Benchmark datasets
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We evaluated the models on the following datasets:
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- [Bangla MMLU](): A private multiple choice question dataset developed by Hishab curated from various sources.
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- [CommonsenseQa Bangla](https://huggingface.co/datasets/hishab/commonsenseqa-bn): 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.
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- [OpenbookQA Bangla](https://huggingface.co/datasets/hishab/openbookqa-bn): 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.
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- [Piqa Bangla](https://huggingface.co/datasets/hishab/piqa-bn): 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.
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- [BoolQ Bangla](https://huggingface.co/datasets/hishab/boolq_bn): 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.
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#### English Benchmark datasets
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- [MMLU](https://huggingface.co/datasets/cais/mmlu): This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge.
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- [CommonseQa](https://huggingface.co/datasets/tau/commonsense_qa): CommonsenseQA is a new multiple-choice question-answering dataset that requires different types of commonsense knowledge to predict the correct answers .
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- [OpenbookQA](https://huggingface.co/datasets/allenai/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.
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- [Piqa](https://huggingface.co/datasets/ybisk/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.
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- [BoolQ](https://huggingface.co/datasets/google/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.
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### Evaluation Results
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#### Evaluation on Bangla Benchmark datasets
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- **titulm-llama-3.2-3b-v2.0** shows improvement in **Commonsense QA** and **PIQA BN**, outperforming the base model in the 5-shot setting, particularly with scores of 0.33 and 0.60, respectively.
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- **llama-3.2-3b** performs slightly better on most tasks, especially in **Bangla MMLU**, where it scores 0.36 in 0-shot and 0.38 in 5-shot, compared to **titulm-llama-3.2-3b-v2.0** scoring lower at 0.26 and 0.24.
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| Model | Shots | Bangla MMLU | BoolQ BN | Commonsense QA BN | OpenBook QA BN | PIQA BN |
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|-------------------------------|---------|-------------|----------|----------------|-------------|---------|
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| llama-3.2-3b | 0-shot | **0.36** | 0.55 | 0.26 | 0.31 | 0.56 |
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| | 5-shot | **0.38** | - | 0.29 | 0.32 | 0.58 |
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| titulm-llama-3.2-3b-v2.0 | 0-shot | 0.26 | **0.57** | **0.27** | **0.32** | **0.58**|
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| | 5-shot | 0.24 | **0.59** | **0.33** | **0.34** | **0.60**|
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#### Evaluation on English Benchmark datasets
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- **llama-3.2-3b** consistently outperforms **titulm-llama-3.2-3b-v2.0** across all English tasks. It achieves high scores, particularly in **MMLU**, **BoolQ**, and **Commonsense QA**, with a maximum score of 0.80 on **PIQA** in the 5-shot setting.
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- In contrast, **titulm-llama-3.2-3b-v2.0** underperforms on all English benchmarks, scoring much lower than the base model, especially in **Commonsense QA** and **PIQA**, with only minor improvements between 0-shot and 5-shot.
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- It was expected as the model was trained only on Bangla datasets.
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| Model | Shots | MMLU | BoolQ | Commonsense QA | OpenBook QA | PIQA |
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|-------------------------------|---------|-------------|--------|----------------|-------------|-------|
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| llama-3.2-3b | 0-shot | **0.54** | **0.73** | **0.64** | **0.43** | **0.77** |
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| | 5-shot | **0.56** | **0.74** | **0.67** | **0.45** | **0.80** |
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| titulm-llama-3.2-3b-v2.0 | 0-shot | 0.24 | 0.49 | 0.20 | 0.22 | 0.57 |
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| | 5-shot | 0.26 | 0.59 | 0.20 | 0.24 | 0.57 |
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### Instruction Tuned Models
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### Intended Use
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- Bangla text generation
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- Bangla language understanding tasks
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- Bangla instruction fine-tuning tasks
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## Citation
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```
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@misc{nahin2025titullmsfamilybanglallms,
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title={TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking},
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author={Shahriar Kabir Nahin and Rabindra Nath Nandi and Sagor Sarker and Quazi Sarwar Muhtaseem and Md Kowsher and Apu Chandraw Shill and Md Ibrahim and Mehadi Hasan Menon and Tareq Al Muntasir and Firoj Alam},
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year={2025},
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eprint={2502.11187},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2502.11187},
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}
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```
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"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:365a0c265893f9ab04f4094f07a13a399b024ab4fc75aadecd0133570d543a6f
|
||||
size 29364254
|
||||
2065
tokenizer_config.json
Normal file
2065
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
19506
trainer_log.jsonl
Normal file
19506
trainer_log.jsonl
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6cda64d3c62fa0b8911a46c7f0b34e90c4af5f57db83e49e0f779a3aec85c10d
|
||||
size 7224
|
||||
Reference in New Issue
Block a user