30th April, 2024

Introducing Pragna-1B: Soket AI Labs' Multilingual Language Model for Indian Languages

Available on Huggingface 🤗: soketlabs/pragna-1b

Introduction: Unleashing the Power of Multilingual AI

We at Soket AI Labs are thrilled to unveil India's first open source multilingual model, Pragna-1B available in four Indian languages - Hindi, Gujarati, Bangla and English. The model is designed to cater to the rich tapestry of Indian languages, significantly expanding the horizons of AI inclusivity and accessibility. As we step into an era where technology transcends linguistic boundaries, Pragna-1B emerges as a beacon of innovation, ready to bridge language barriers and enhance user engagement across diverse linguistic landscapes.

Highlights of Pragna-1B:

• Architecture: Pragna-1B features a Transformer Decoder-only model with 1.25 billion parameters, with a context length of 2048 tokens.
• Designed for Edge AI: Engineered to deliver state-of-the-art performance for vernacular languages in the smallest form factor, ideal for deployment on-device.
• Small Language Model (SLM) with Robust Capabilities: Even with 1.25 parameters, Pragna-1B’s performance is comparable to larger 7 billion parameter sized models, offering comprehensive multilingual support for English, Hindi, Bangla, and Gujarati.
• Culturally Contextualised Training: The model has been meticulously trained on curated datasets specifically designed to encompass the Indian context, ensuring accurate and culturally relevant outputs.
• Ethical and Responsible AI: Committed to upholding human values, the model is aligned to generate ethical responses.
• Open Source Availability: The base version of Pragna-1B is accessible as an open-source model on Hugging Face, facilitating development and collaboration within the community.

Architecture Overview

Pragna-1B is a decoder-only transformer model inspired by TinyLlama, featuring the following specifications:

• Layers: 22
• Attention Heads: 32
• Context Length: 2048
• Hidden Dimension: 2048
• Expansion Dimension: 5632
• Vocabulary Size: 69632

This model incorporates Rotary Positional Encoding to infuse positional information into the embeddings, utilising a base of 10,000. It employs RSNorm with an epsilon value of 1e-5 and the Sigmoid Activation Unit (SiLU) as the activation function. Additionally, Pragna-1B adopts Grouped Query Attention, an alternative to Multi-Head Attention, which enhances training and inference speed while reducing memory bandwidth. This also supports the use of lower-compute devices for inference tasks.

Pragna-1B is trained on our proprietary platform, GenAI Studio, a modular AI Developer Platform designed to support any GenAI model architecture. It is capable of scaling across thousands of GPUs or accelerators and is built to be fault-tolerant. The development of this model leveraged Triton, an open-source language from OpenAI, for crafting high-performance custom fused CUDA Kernels for various operations. Furthermore, the model uses Fully Sharded Data Parallel (FSDP) for distributed and parallel training and incorporates the state-of-the-art FlashAttention2 to accelerate training and inference.

Developing an Efficient Tokenizer for Indian Languages

Pragna employs a Byte-Pair Encoding (BPE) tokenizer, specifically trained for handling Indian languages. The tokenizer was trained on six Indian languages—Hindi, Bangla, Urdu, Tamil, Kannada, and Gujarati—before combining them into a unified tokenizer. This unification was achieved through a union set operation, which ensures each language is represented equitably. The merging rules were selected to optimise compression of text into tokens, effectively making the vocabulary size 69,632.

The tokenizer demonstrates its efficiency through its fertility score, a metric that measures the average number of tokens produced per word. This score is crucial as it reflects the tokenizer's ability to compress text into tokens—computer representable integers—with a lower score indicating more efficient tokenization. Notably, our tokenizer not only performs comparably to those designed for English but excels in processing Hindi, Bangla, Gujarati, Tamil, Kannada, and Urdu as shown in the graph below. For example, the Gemma-7b model produces 5.8 tokens per word whereas Pragna-1b generates 2.8 tokens per word for Kannada almost doubling the throughput just with tokenization.

Moreover, our tokenizer addresses a significant issue prevalent in existing models: the inadequate representation of Indic languages. Traditional models often dissect Indic characters into bytes, severely hindering performance and requiring an excessive number of tokens for effective training. In contrast, our approach significantly enhances model efficiency and performance across Indian languages.

Graph above shows the fertility score for six Indian languages when tokenized using various LLMs.

Training Data

The quality and quantity of training data are crucial in imparting linguistic and semantic understanding into any language model. One significant challenge in developing language models for Indian languages is the scarcity of large-scale corpora. With all 22 scheduled Indian languages representing less than 1% of internet-scale datasets like mC4, we were compelled to create "Bhasha," a series of high-quality datasets designed for pretraining and instruction fine-tuning of Indian models.

Bhasha-wiki: Starting with 6.3 million English Wikipedia articles, we translated these into six Indian languages to create a dataset of 44.1 million articles. With over 45.1 billion Indic tokens, Bhasha-wiki serves as a foundational resource for linguistic and AI research. It supports a broad spectrum of applications, including machine translation, natural language processing, and language model training.

Bhasha-wiki-indic: A refined subset of the Bhasha-wiki, Bhasha-wiki-indic is curated to enrich models with an in-depth understanding of the Indian context. This subset specifically includes content of significant relevance to India, aiming to develop culturally resonant AI applications.

Bhasha-SFT: Designed to facilitate the development of language models capable of handling various NLP tasks such as multi-turn conversation, question-answering, text summarization, context-based Q&A, and natural language generation, the Bhasha-SFT dataset provides essential tools for enhancing language model performance across diverse linguistic landscapes.

We have also incorporated external datasets like SlimPajama, a clean and deduplicated dataset by Cerebras featuring 627 billion tokens with a majority in English, and Sangraha-Verified by AI4Bharat, a 15 million tuple dataset in multiple Indian languages, curated from human-verified sources.

Training

Model Weight Initialization: Instead of starting from a random distribution, we initialise the weights using TinyLlama-1.1B-Chat-v1.0, a model under the Apache-2.0 licence. This approach leverages the foundational knowledge of an existing model, reducing costs and facilitating the transfer learning from the English domain to the Indian language domain.

Indic Tokenizer: Our base tokenizer, borrowed from Llama-2, initially had a vocabulary size of 32,000. To this, we added new tokens for six Indian languages, expanding the vocabulary to 69,632. This enhancement significantly improved the fertility score across all six languages.

Embedding Initialization for New Tokens: Drawing inspiration from John Hewitt’s research, "Initializing New Word Embeddings for Pretrained Language Models", we opted for a targeted approach to initialising embeddings for new tokens. Instead of averaging all existing embeddings with added noise, we used a subset of relevant embeddings. This method assumes each new Indic token can be tokenized using Llama-2's existing vocabulary. By averaging these related embedding vectors, we achieved a more accurate representation with lower KL-divergence. We are preparing a detailed blog post to share our methodology and findings on efficient vocabulary expansion.

Embedding Alignment: Initially, we aligned only the embedding and lm_head, keeping other tensors frozen. We utilised a parallel sentences dataset from Bhasha-wiki, pairing sentences in one of the six Indian languages with their English counterparts. Loss was computed solely on the English sentences, aiding in the alignment of model embeddings and facilitating the generation of coherent though nonsensical text in all six languages with minimal training.

Continual Pretraining: We subsequently enabled all 1.25 billion parameters for further training, focusing on three languages—Hindi, Bangla, and Gujarati—due to compute limitations. We maintained high, equal sampling probabilities for these languages while incorporating data from SlimPajama, predominantly in English. The model processed approximately 150 billion tokens (3.15 trillion considering the base model) over about 8,000 GPU-hours. Training specifics included 3,000 hours on 4xA100-40 GiB and 5,000 hours on 8xA100-80 GiB systems.

Training Parameters:

• Precision: BFloat16
• Batch Size: 2k - 2.5k
• Context Length: 2,048
• Learning Rate: 3e-5
• Optimizer: AdamW
• LR Scheduler: Cosine
• Mixed Precision Training

Instruction Fine-Tuning: The model is undergoing supervised fine-tuning across multiple tasks, including conversation, question-answering, summarization, and paraphrasing using Bhasha-SFT which comprises over 13 million instances of instruction-response data in three Indian languages from Aya Collection, Indic-align, and Samvaad.

Ethics and Safety Alignment: Safety and ethics alignment is an important part of the supervised fine–tuning step. To make sure Pragna is aligned to human values, we have been working on building our own datasets specifically curated to prevent the model from generating unethical or potentially harmful content. This data is being exposed to the model during SFT.

Ongoing Developments and Community Engagement: As the model is currently undergoing further instruction tuning and safety alignment, we plan to release pragna-1b-it model weights to the community under an open-source licence soon. An initial research preview of the instruction-tuned model is available via a chat interface, though it is not recommended for production use due to its tendency to generate convincing yet factually incorrect information. We eagerly anticipate feedback from the community to further refine and enhance our models.

Inference

Next Token Prediction

SFT outputs

Research Preview: spaces/pragna-chat

Disclaimer: An initial checkpoint of the instruction tuned model is made available as a research preview. It is hereby cautioned that the model has the potential to produce hallucinatory and plausible yet inaccurate statements. Users are advised to exercise discretion when utilizing the generated content.

Performance and Benchmarking

Eval numbers for Hindi and Gujarati are taken from Indic LLM Leaderboard and English from HF's Open LLM Leaderboard. We are running benchmarks for Bangla and will be updated soon.

Pragna-1b, boasting 1.25 billion parameters, demonstrates performance akin to that of more expansive models such as OpenHathi-7b and Llama-2-7b, and falls within a comparable range with gemma-7b. However, there is a trade-off in English language proficiency when compared to the base model, TinyLlama. This difference arises from the model's integration of extensive information from three Indian languages into the same parameter space. Essentially, Pragna-1b manages to encapsulate a vast corpus of global knowledge, totaling close to 1 TiB, within a remarkably constrained latent space of just 2.3 GiB (BFloat16).

These are preliminary benchmarking results for Pragna-1B. However, for Indian language benchmarking, we have to be careful in interpreting these figures, as the quality of benchmarking data is currently suboptimal. This limitation largely stems from the reliance on datasets generated through machine translations. We are working to develop comprehensive benchmarks that assess both the linguistic understanding and contextual intelligence of large language models across a variety of fields and tasks for all Indian languages.

Pragna-1B achieves substantial speed enhancements, demonstrating approximately 3x, 7x, 3x, and 6.5x acceleration for Hindi, Kannada, Bangla, and Gujarati, respectively, relative to Llama-2-7B when considering efficient tokenization alone. The actual throughput and speed improvements are expected to be even greater, owing to reduced computational demands facilitated by a parameter count nearly 7 times lower than that of Llama-2-7B.

Future Work

Alignment for Factual Accuracy: While the model demonstrates good linguistic understanding, it is imperative to align it more closely with factual veracity. Addressing instances of factual hallucination remains a top priority, as we strive to ensure that the model consistently delivers accurate and reliable information.

Expansion of Linguistic Scope: In our pursuit of linguistic inclusivity, we aim to broaden the knowledge base of the model by incorporating additional Indian languages. This expansion involves not only introducing more linguistic tokens but also deepening the model's understanding of the nuances inherent in diverse language structures.

Mixture of Experts for Multilingual Proficiency: To facilitate seamless communication across a spectrum of languages, we are exploring the implementation of a Mixture of Experts approach. This will enable the model to adeptly correlate complexities of multilingual interactions, thereby enriching its linguistic capabilities.

Architectural Innovation and Experimentation: Being a research first team, we will strategise researching and experimenting with novel architectures to innovative solutions and optimise the model's performance across various domains and use cases.

Decoupling Knowledge and Language Understanding: A key focus of our future endeavours involves refining the model's architecture to effectively separate knowledge acquisition from language understanding. By delineating these two components within the model's architecture, we aim to bolster its capacity for multilingual knowledge transfer.

Distillation and Quantization for Efficiency: Focussing on the importance of efficiency without compromising performance, we will be developing distilled and quantized versions of the model. These streamlined iterations harness the power of compression techniques to maximise computational efficiency while upholding the model's performance.

Conclusion

Pragna-1B's introduction holds significant promise for India, promising to revolutionize sectors like education, governance, and commerce. By supporting Indian languages, it fosters inclusivity, expands access to information, and empowers communities in the digital economy. As it evolves, Pragna-1B can bridge linguistic divides, foster cross-cultural understanding, and drive socioeconomic development, making a lasting impact on India's digital landscape.