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---
library_name: pytorch
license: apache-2.0
pipeline_tag: text-generation
tags:
- llm
- generative_ai
- android
---
# IBM-Granite-3B-Code-Instruct: Optimized for Mobile Deployment
## State-of-the-art large language model useful on a variety of code understanding and generation tasks
Granite-3B-Code-Instruct-2K is a 3B parameter model fine tuned from Granite-3B-Code-Base-2K on a combination of permissively licensed instruction data to enhance instruction following capabilities including logical reasoning and problem-solving skills.
This model is an implementation of IBM-Granite-3B-Code-Instruct found [here](https://huggingface.co/ibm-granite/granite-3b-code-instruct-2k).
More details on model performance accross various devices, can be found [here](https://aihub.qualcomm.com/models/ibm_granite_3b_code_instruct).
### Model Details
- **Model Type:** Text generation
- **Model Stats:**
- Input sequence length for Prompt Processor: 128
- Context length: 2048
- Number of parameters: 3.48B
- Precision: fp16
- Num of key-value heads: 32
- Information about the model parts: Prompt Processor and Token Generator are split into 4 parts each. Each corresponding Prompt Processor and Token Generator part share weights.
- Prompt processor model size: 7 GB
- 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 model size: 7 GB
- 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: 2.27.7
- 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.
| Model | Device | Chipset | Target Runtime | Response Rate (tokens per second) | Time To First Token (range, seconds)
|---|---|---|---|---|---|
| IBM-Granite-3B-Code | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 5.47 | 0.3262 - 5.2192 | -- | -- |
## License
* The license for the original implementation of IBM-Granite-3B-Code-Instruct can be found [here](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md).
* The license for the compiled assets for on-device deployment 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-3b-code-instruct-2k)
## Community
* Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:[email protected]).
## 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
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