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What is Sarvam-M?

Sarvam-M is a cutting-edge multilingual large language model designed to excel in a variety of Indian languages while seamlessly tackling complex mathematical and programming tasks within a unified framework. Built upon the Mistral-Small architecture, it features a powerful configuration with 24 billion parameters and has undergone extensive refinement through methods like supervised fine-tuning and reinforcement learning, ensuring both accuracy and efficiency. This model is expertly crafted to support over ten major Indic languages, effectively managing native scripts, romanized text, and code-mixed entries, which promotes fluid multilingual communication across diverse settings. Furthermore, Sarvam-M incorporates a hybrid reasoning approach that allows it to switch between an in-depth “thinking” mode for challenging problems, such as mathematics and logic puzzles, and a quick response mode for more routine questions, striking an optimal balance between rapidity and performance. As such, Sarvam-M stands out as an essential resource for users who wish to navigate an increasingly varied linguistic landscape, enhancing their interaction with technology in meaningful ways. Its innovative design positions it as a key player in advancing language model capabilities in the realm of multilingual applications.

What is Chinchilla?

Chinchilla represents a cutting-edge language model that operates within a compute budget similar to Gopher while boasting 70 billion parameters and utilizing four times the amount of training data. This model consistently outperforms Gopher (which has 280 billion parameters), along with other significant models like GPT-3 (175 billion), Jurassic-1 (178 billion), and Megatron-Turing NLG (530 billion) across a diverse range of evaluation tasks. Furthermore, Chinchilla’s innovative design enables it to consume considerably less computational power during both fine-tuning and inference stages, enhancing its practicality in real-world applications. Impressively, Chinchilla achieves an average accuracy of 67.5% on the MMLU benchmark, representing a notable improvement of over 7% compared to Gopher, and highlighting its advanced capabilities in the language modeling domain. As a result, Chinchilla not only stands out for its high performance but also sets a new standard for efficiency and effectiveness among language models. Its exceptional results solidify its position as a frontrunner in the evolving landscape of artificial intelligence.

Media

Media

No images available

Integrations Supported

Sarvam AI

Integrations Supported

Google Stitch
MusicFX
WeatherNext

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Sarvam

Date Founded

2023

Company Location

India

Company Website

www.sarvam.ai/blogs/sarvam-m

Company Facts

Organization Name

Google DeepMind

Company Location

United States

Company Website

arxiv.org/abs/2203.15556

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

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