sarvam-1

Multilingual audio to text model

Sarvam-30B is an advanced Mixture-of-Experts (MoE) model with 2.4B non-embedding active parameters, designed primarily for practical deployment. It combines strong reasoning, reliable coding ability, and best-in-class conversational quality across Indian languages. Sarvam-30B is built to run reliably in resource-constrained environments and can handle multilingual voice calls while performing tool calls.

Sarvam-105B is an advanced Mixture-of-Experts (MoE) model with 10.3B active parameters, designed for superior performance across a wide range of complex tasks. It is highly optimized for complex reasoning, with particular strength in agentic tasks, mathematics, and coding.

Multilingual, hybrid-reasoning, text-only language model built on Mistral-Small

Translation model for 22 Indian Languages

Multilingual audio to text model

LUCAN 10B Physics MoE is an 11B parameter multi-domain scientific foundation model built on a hybrid Mamba2 state-space and 12-layer Sparse Mixture-of-Experts (MoE) architecture with a PhysicsConstrainedOutputHead. It models continuous scientific latents across fluid transport, chemical reactors, phase-change thermal physics, and molecular systems.

LUCAN 3B is an approximately 3-billion-parameter scientific AI model from Shodh AI for processing numerical reactor data. This release includes the model weights, dedicated tokenizers and matching adapters for bioreactor and continuous-flow reactor applications, along with loading instructions and an introductory guide.

This Machine Translation (MT) model translates text from one language to another using a trained neural network. It can support multilingual communication and language translation applications. Translation quality depends on language coverage, training data, and contextual accuracy. For Mizo, evaluate the model using representative test sentences before deployment.

COILD Multilingual Translation Model (COILD-Mul-MT)

GNM (pronounced genome) is a state-of-the-art parametric 3D statistical model of the human head developed by Google. Learned from a large dataset of high-resolution 3D scans, GNM provides fine-grained, disentangled control over facial identity, expressions, and head pose, complete with controllable internal anatomy (eyeballs, teeth, and tongue). The model is released under the Apache 2.0 permissive license, suitable for both academic research and commercial applications.

VITS text-to-speech models for Sourashtra, a low-resource Indo-Aryan language. There are male and female voices, and each voice accepts text in either Tamil script or Sourashtra script.

DiffusionGemma is a generative model built by Google DeepMind. Based on the 26B A4B Mixture-of-Experts (MoE) Gemma 4 architecture, DiffusionGemma generates tokens using discrete diffusion. This open-weights model is multimodal, handling text, image, and video inputs to generate text output.

Automatic Speech Recognition (ASR) model for speech recognition, processing audio and transcribing spoken content into text.The inference code, installation requirements, and usage instructions are available in the SPRING Lab, IIT Madras GitHub repository: https://github.com/Speech-Lab-IITM/Fairseq-Inference

Automatic Speech Recognition (ASR) model for speech recognition, processing audio and transcribing spoken content into text.The inference code, installation requirements, and usage instructions are available in the SPRING Lab, IIT Madras GitHub repository: https://github.com/Speech-Lab-IITM/Fairseq-Inference

A transformer-based model pre-trained on multiple Indian languages using Masked Language Modeling and Sampling-based Translation Language Modeling techniques.

A transformer-based model pre-trained exclusively on Masked Language Modeling tasks across multiple Indian languages.

A transformer-based model pre-trained on large-scale corpora, covering 12 major Indian languages.

A transformer-based model pre-trained on multiple Indian languages, focusing on Sentence Similarity tasks.

A transformer-based model pre-trained on multiple Indian languages using Masked Language Modeling and Back-Translation Language Modeling techniques.

Khasi-English semantic search model, trained on 66,794 pairs with 0.69-0.74 similarity. ~90MB, supports Meghalaya tourism/culture. By MWirelabs

RomanSetu is a multilingual continual pretrained transformer model designed for transliteration across six Indic languages

RomanSetu is a multilingual continual pretrained transformer model designed for transliteration across six Indic languages

RomanSetu is a multilingual continual pretrained transformer model designed for transliteration across six Indic languages

RomanSetu is a multilingual continual pretrained transformer model designed for transliteration across six Indic languages