v0.47.0

Deprecation notice.

LICENSE

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-The license of the original trained model can be found at https://huggingface.co/microsoft/Phi-3.5-mini-instruct/blob/main/LICENSE.

README.md

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 library_name: pytorch
 license: other
 tags:
-- llm
-- generative_ai
-- android
-pipeline_tag: text-generation
+- deprecated
+pipeline_tag: other
 
 ---
-
-![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/phi_3_5_mini_instruct/web-assets/model_demo.png)
-
-# Phi-3.5-Mini-Instruct: Optimized for Qualcomm Devices
-
-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 is based on the implementation of Phi-3.5-Mini-Instruct found [here](https://huggingface.co/microsoft/Phi-3.5-mini-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/phi_3_5_mini_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 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.
-
-## Getting Started
-Download pre-exported model assets from **[Phi-3.5-Mini-Instruct on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/phi_3_5_mini_instruct)**.
-
-
-
-## Model Details
-
-**Model Type:** Model_use_case.text_generation
-
-**Model Stats:**
-- Input sequence length for Prompt Processor: 128
-- Context length: 4096
-- Number of parameters: 3.8B
-- Precision: w4a16 + w8a16 (few layers)
-- 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.
-
-## Performance Summary
-| Model | Runtime | Precision | Chipset | Context Length | Response Rate (tokens per second) | Time To First Token (range, seconds)
-|---|---|---|---|---|---|---
-| Phi-3.5-Mini-Instruct | QNN_CONTEXT_BINARY | w4a16 | Snapdragon® 8 Elite Mobile | 4096 | 14.73 | 0.1195948 - 3.8270336
-| Phi-3.5-Mini-Instruct | QNN_CONTEXT_BINARY | w4a16 | Snapdragon® X Elite | 4096 | 6.2 | 0.185833 - 5.946656
-| Phi-3.5-Mini-Instruct | QNN_CONTEXT_BINARY | w4a16 | Snapdragon® 8 Gen 3 Mobile | 4096 | 13.01 | 0.1469056 - 4.7009792
-
-## 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).
-
-## 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://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
+This model is deprecated. Please refer to https://aihub.qualcomm.com for the latest models and updates.