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README.md

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+---
+library_name: pytorch
+license: mit
+tags:
+- llm
+- generative_ai
+- quantized
+- android
+pipeline_tag: text-generation
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/phi_3_5_mini_instruct_quantized/web-assets/model_demo.png)
+
+# Phi-3.5-mini-instruct: Optimized for Mobile Deployment
+## State-of-the-art large language model useful on a variety of language understanding and generation tasks
+
+
+Phi-3.5-mini is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data. The model belongs to the Phi-3 model family and supports 128K token context length. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures.
+
+This model is an implementation of Phi-3.5-mini-instruct found [here](https://huggingface.co/microsoft/Phi-3.5-mini-instruct).
+
+
+ More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/phi_3_5_mini_instruct_quantized).
+
+### Model Details
+
+- **Model Type:** Text generation
+- **Model Stats:**
+  - Input sequence length for Prompt Processor: 128
+  - Context length: 4096
+  - Number of parameters: None
+  - Precision: w4a16 + w8a16 (few layers)
+  - Num of key-value heads: 8
+  - 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: 2.16 GB
+  - Token generator model size: 2.16 GB
+  - Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
+  - Supported languages: English, Arabic, Chinese, Dutch, French, German, Italian, Russian, Spanish, Ukranian
+  - Minimum QNN SDK version required: 2.28.2
+  - 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 (4096 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)
+|---|---|---|---|---|---|
+| Phi-3.5-mini-instruct | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 13.01 | 0.1469056 - 4.7009792 | -- | [Phi-3.5-mini-instruct.bin](https://huggingface.co/qualcomm/Phi-3.5-mini-instruct/blob/main/Phi-3.5-mini-instruct.bin) |
+| Phi-3.5-mini-instruct | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 6.2 | 0.185833 - 5.946656 | -- | Use Export Script |
+| Phi-3.5-mini-instruct | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 14.73 | 0.1195948 - 3.8270336 | -- | Use Export Script |
+
+## Deploying Phi-3.5-mini-instruct on-device
+
+Please follow the [LLM on-device deployment](https://github.com/quic/ai-hub-apps/tree/main/tutorials/llm_on_genie) tutorial.
+
+
+
+## License
+* The license for the original implementation of Phi-3.5-mini-instruct can be found
+  [here](https://huggingface.co/microsoft/Phi-3.5-mini-instruct/blob/main/LICENSE).
+* The license for the compiled assets for on-device deployment can be found [here](https://github.com/baichuan-inc/Baichuan-7B/blob/main/LICENSE)
+
+
+
+## References
+* [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://arxiv.org/abs/2404.14219)
+* [Source Model Implementation](https://huggingface.co/microsoft/Phi-3.5-mini-instruct)
+
+
+
+## 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:ai-hub-support@qti.qualcomm.com).
+
+## 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