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.

More details on model performance accross 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

Community

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
Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Examples
Inference API (serverless) does not yet support pytorch models for this pipeline type.