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README.md

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+---
+library_name: pytorch
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- llm
+- generative_ai
+- android
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ibm_granite_3b_code_instruct/web-assets/model_demo.png)
+
+# 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 is based on the implementation of IBM-Granite-3B-Code-Instruct found
+[here]({source_repo}). 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.
+  - Tiny MMLU: Tiny MMLU (Massive Multitask Language Understanding) is an English language benchmark designed to measure knowledge acquired during pretraining by evaluating models exclusively in zero-shot and few-shot settings. This makes the benchmark more challenging and more similar to how we evaluate humans.
+
+| 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