|
|
--- |
|
|
library_name: pytorch |
|
|
license: other |
|
|
tags: |
|
|
- llm |
|
|
- generative_ai |
|
|
- android |
|
|
pipeline_tag: text-generation |
|
|
|
|
|
--- |
|
|
|
|
|
 |
|
|
|
|
|
# Qwen2-7B-Instruct: Optimized for Qualcomm Devices |
|
|
|
|
|
The Qwen2-7B-Instruct is a state-of-the-art multilingual language model with 7.07 billion parameters, excelling in language understanding, generation, coding, and mathematics. |
|
|
|
|
|
This is based on the implementation of Qwen2-7B-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_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 Qwen2-7B-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 |
|
|
Download pre-exported model assets from **[Qwen2-7B-Instruct on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/qwen2_7b_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: 7.07B |
|
|
- Precision: w4a16 + w8a16 (few layers) |
|
|
- Information about the model parts: Prompt Processor and Token Generator are split into 5 parts each. Each corresponding Prompt Processor and Token Generator part share weights. |
|
|
- Supported languages: English, Chinese, German, French, Spanish, Portuguese, Italian, Dutch, Russian, Czech, Polish, Arabic, Persian, Hebrew, Turkish, Japanese, Korean, Vietnamese, Thai, Indonesian, Malay, Lao, Burmese, Cebuano, Khmer, Tagalog, Hindi, Bengali, Urdu. |
|
|
- 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) |
|
|
|---|---|---|---|---|---|--- |
|
|
| Qwen2-7B-Instruct | QNN_CONTEXT_BINARY | w4a16 | Snapdragon® 8 Elite Mobile | 4096 | 13.65 | 0.170593 - 5.458976 |
|
|
|
|
|
## License |
|
|
* The license for the original implementation of Qwen2-7B-Instruct can be found |
|
|
[here](https://huggingface.co/Qwen/Qwen2-7B-Instruct/blob/main/LICENSE). |
|
|
|
|
|
## References |
|
|
* [Qwen2 Technical Report](https://arxiv.org/abs/2407.10671v1) |
|
|
* [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 |
|
|
|
