---
library_name: pytorch
license: other
tags:
- llm
- generative_ai
- android
pipeline_tag: text-generation

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ibm_granite_v3_1_8b_instruct/web-assets/model_demo.png)

# IBM-Granite-v3.1-8B-Instruct: Optimized for Qualcomm Devices

Granite-3.1-8B-Instruct is a 8B parameter long-context instruct model finetuned from Granite-3.1-8B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging.

This is based on the implementation of IBM-Granite-v3.1-8B-Instruct found [here](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/ibm_granite_v3_1_8b_instruct) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.

## Deploying IBM Granite 3.1 on-device

Please follow the [LLM on-device deployment](https://github.com/quic/ai-hub-apps/tree/main/tutorials/llm_on_genie) tutorial.

## Getting Started
Due to licensing restrictions, we cannot distribute pre-exported model assets for this model.
Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/ibm_granite_v3_1_8b_instruct) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations

See our repository for [IBM-Granite-v3.1-8B-Instruct on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/ibm_granite_v3_1_8b_instruct) for usage instructions.


## Model Details

**Model Type:** Model_use_case.text_generation

**Model Stats:**
- Input sequence length for Prompt Processor: 128
- Context length: 4096
- Precision: w4a16 + w8a16 (few layers)
- Num of key-value heads: 8
- Information about the model parts: Prompt Processor and Token Generator are split into 5 parts each. Each corresponding Prompt Processor and Token Generator part share weights.
- Prompt processor input (part1): 128 tokens
- Prompt processor output (part1): Embeddings output
- Prompt processor input (other parts): 128 tokens + KVCache initialized with pad token
- Prompt processor output (other parts): 128 output tokens + KVCache for token generator
- Token generator input (part1): 1 token
- Token generator output (part1): Embeddings output
- Token generator input (other parts): 1 input token + past KVCache
- Token generator output (other parts): 1 output token + KVCache for next iteration
- Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
- Supported natural languages: English
- Supported programming languages: The Granite code foundation models support 116 programming languages including Python, Javascript, Java, C++, Go, and Rust.
- Minimum QNN SDK version required: 2.3
- 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 (2048 tokens).
- Response Rate: Rate of response generation after the first response token.

## Performance Summary
| Model | Runtime | Precision | Chipset | Context Length | Response Rate (tokens per second) | Time To First Token (range, seconds)
|---|---|---|---|---|---|---
| IBM-Granite-v3.1-8B-Instruct | QNN_CONTEXT_BINARY | w4a16 | Snapdragon® 8 Elite Mobile | 4096 | 11.01293 | 0.19679249999999998 - 6.297359999999999
| IBM-Granite-v3.1-8B-Instruct | QNN_CONTEXT_BINARY | w4a16 | Snapdragon® X Elite | 4096 | 8.01724 | 0.2953902 - 9.4524864

## License
* The license for the original implementation of IBM-Granite-v3.1-8B-Instruct can be found
  [here](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md).

## References
* [Granite Code Models: A Family of Open Foundation Models for Code Intelligence](https://arxiv.org/abs/2405.04324)
* [Source Model Implementation](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct)

## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).

## 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