Sarvam ai has recently introduced its cutting-edge language model, Sarvam-2BThis powerful model, boasting 2 billion parameters, represents a significant advancement in Indic language processing. With a focus on cultural inclusion and representation, Sarvam-2B is pre-trained from scratch on a massive dataset of 4 trillion high-quality tokens, with an impressive 50% dedicated to Indic languages. This development, particularly its ability to understand and generate text in languages, is historically underrepresented in ai research.
They have also introduced the Samvaad-Hi-v1 datasetA carefully curated collection of 100,000 high-quality conversations in English, Hindi, and Hinglish. This dataset is uniquely designed with an Indic context, making it an invaluable resource for researchers and developers working on multilingual and culturally relevant ai models. Samvaad-Hi-v1 is poised to enhance the training of conversational ai systems that can understand and interact with users in a more natural and contextually appropriate manner across different languages and dialects prevalent in India.
The vision behind Sarvam-2B
Sarvam ai’s vision with Sarvam-2B is clear: to create a robust and versatile language model that excels in English and is a benchmark for Indian languages. This is especially important in a country like India, where linguistic diversity is huge and the need for ai models that can process and generate texts in multiple languages is paramount.
The model supports 10 Indic languages including Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, and Telugu. This broad language support ensures that the model is accessible to many users from different linguistic backgrounds. The architecture and training process of the model have been meticulously designed to ensure good performance across all supported languages, making it a versatile tool for developers and researchers.
Technical excellence and implementation
Sarvam-2B has been trained on a balanced mix of English and Indic data, each contributing 2 trillion tokens to the training process. This careful balance ensures that the model is equally proficient in English and the supported Indic languages. The training process involved sophisticated techniques to improve the model’s understanding and generation capabilities, making it one of the most advanced models in its category.
Broadening the horizon: complementary models
In addition to Sarvam-2B, Sarvam ai has also introduced three other notable models that complement its capabilities:
- Bulbul 1.0: A text-to-speech (TTS) model that supports combinations of 10 languages and six voices. This model generates natural-sounding speech, making it a valuable tool for applications that require multilingual voice output.
- Saras 1.0: A speech-to-text (STT) model that supports the same ten languages and includes automatic language identification. This model is particularly useful for transcribing spoken language into text, with the added benefit of automatically detecting the language.
- Mayura 1.0: A translation API designed to handle the complexities of translation between Indian languages and English. This model is designed to address the unique nuances and challenges associated with Indian languages, providing more accurate and culturally relevant translations.
Conclusion
Sarvam ai launched Sarvam-2B, particularly in the context of language models designed for Indic languages. By dedicating half of its training data to these languages, Sarvam-2B stands out as a model that actively promotes the importance of linguistic diversity. The versatility of the model, combined with the complementary capabilities of Bulbul 1.0, Saaras 1.0, and Mayura 1.0, positions Sarvam ai as a leader in developing inclusive, innovative, and forward-thinking ai technologies.
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Asif Razzaq is the CEO of Marktechpost Media Inc. As a visionary engineer and entrepreneur, Asif is committed to harnessing the potential of ai for social good. His most recent initiative is the launch of an ai media platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is technically sound and easily understandable to a wide audience. The platform has over 2 million monthly views, illustrating its popularity among the public.
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