IndusQ-1.1B: Optimized for Mobile Deployment

State-of-the-art large language model useful on a variety of language understanding and generation tasks

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

This model is an implementation of IndusQ-1.1B found here.

Please contact us to purchase this model. More details on model performance across various devices, can be found here.

Model Details

• Model Type: Text generation
• Model Stats:
  • Input sequence length for Prompt Processor: 128
  • Max context length: 1024
  • Number of parameters: 1B
  • Precision: w4a16 + w8a16 (few layers)
  • Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
  • Minimum QNN SDK version required: 2.27.7
  • 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).
  • Response Rate: Rate of response generation after the first response token.

Model	Device	Chipset	Target Runtime	Response Rate (tokens per second)	Time To First Token (range, seconds)
IndusQ-1.1B	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	74.6	0.028561 - 0.228489

Deploying IndusQ-1.1B on-device

Please follow the LLM on-device deployment tutorial.

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

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