| | --- |
| | library_name: pytorch |
| | license: other |
| | tags: |
| | - llm |
| | - generative_ai |
| | - android |
| | pipeline_tag: text-generation |
| |
|
| | --- |
| | |
| |  |
| |
|
| | # Qwen2.5-7B-Instruct: Optimized for Qualcomm Devices |
| |
|
| | The Qwen2.5-7B-Instruct is a state-of-the-art multilingual language model with 7 billion parameters, excelling in language understanding, generation, coding, and mathematics. |
| |
|
| | This is based on the implementation of Qwen2.5-7B-Instruct found [here](https://github.com/QwenLM/Qwen2.5). |
| | 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/qwen2_5_7b_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 Qwen2.5-7B-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 |
| | There are two ways to deploy this model on your device: |
| |
|
| | ### Option 1: Download Pre-Exported Models |
| |
|
| | Download pre-exported model assets from **[Qwen2.5-7B-Instruct on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/qwen2_5_7b_instruct)**. |
| |
|
| |
|
| | ### Option 2: Export with Custom Configurations |
| |
|
| | Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/qwen2_5_7b_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 |
| |
|
| | This option is ideal if you need to customize the model beyond the default configuration provided here. |
| |
|
| | See our repository for [Qwen2.5-7B-Instruct on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/qwen2_5_7b_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 |
| | - Quantization Type: w4a16 + w8a16 (few layers) |
| | - Supported languages: Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. |
| | - 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) |
| | |---|---|---|---|---|---|--- |
| | | Qwen2.5-7B-Instruct | QNN_CONTEXT_BINARY | w4a16 | Snapdragon® 8 Elite Mobile | 4096 | 15.40274 | 0.1356538 - 4.3409216 |
| | | Qwen2.5-7B-Instruct | QNN_CONTEXT_BINARY | w4a16 | Snapdragon® X Elite | 4096 | 12.33811 | 0.1749494 - 5.5983808 |
| | |
| | ## License |
| | * The license for the original implementation of Qwen2.5-7B-Instruct can be found |
| | [here](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE). |
| | |
| | ## References |
| | * [Qwen2.5 Technical Report](https://arxiv.org/abs/2412.15115) |
| | * [Source Model Implementation](https://github.com/QwenLM/Qwen2.5) |
| | |
| | ## 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 |
| | |
