IndusQ-1.1B

IndusQ-1.1B: Optimized for Qualcomm Devices

Indus is today a 1.2 billion parameter model and has been supervised fine tuned for Hindi and dialects.

This is based on the implementation of IndusQ-1.1B found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

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Getting Started

This model is available for purchase. Please contact us to learn more about licensing options.

Getting Started

This model is available for purchase. Please contact us to learn more about licensing options.

Model Details

Model Type: Model_use_case.text_generation

Model Stats:

• Input sequence length for Prompt Processor: 128
• Max context length: 1024
• Minimum QNN SDK version required: 2.27.7
• Number of parameters: 1B
• Precision: w4a16 + w8a16 (few layers)
• Response Rate: Rate of response generation after the first response token.
• Supported languages: Hindi and English.
• TTFT: Time To First Token is the time it takes to generate the first response token. This is expressed as a range because it varies based on the length of the prompt. The lower bound is for a short prompt (up to 128 tokens, i.e., one iteration of the prompt processor) and the upper bound is for a prompt using the full context length (1024 tokens).
• Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.

Performance Summary

References

• Project Indus: A Foundational Model for Indian Languages
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us.

Usage and Limitations

This model may not be used for or in connection with any of the following applications:

• Accessing essential private and public services and benefits;
• Administration of justice and democratic processes;
• Assessing or recognizing the emotional state of a person;
• Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics;
• Education and vocational training;
• Employment and workers management;
• Exploitation of the vulnerabilities of persons resulting in harmful behavior;
• General purpose social scoring;
• Law enforcement;
• Management and operation of critical infrastructure;
• Migration, asylum and border control management;
• Predictive policing;
• Real-time remote biometric identification in public spaces;
• Recommender systems of social media platforms;
• Scraping of facial images (from the internet or otherwise); and/or
• Subliminal manipulation