---
library_name: pytorch
license: llama2
pipeline_tag: text-generation
tags:
- llm
- generative_ai
- quantized
- android

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/llama_v2_7b_chat_quantized/web-assets/model_demo.png)

# Llama-v2-7B-Chat: Optimized for Mobile Deployment
## State-of-the-art large language model useful on a variety of language understanding and generation tasks

Llama 2 is a family of LLMs. The "Chat" at the end indicates that the model is optimized for chatbot-like dialogue. The model is quantized to 4-bit weights and 16-bit activations making it suitable for on-device deployment. For Prompt and output length specified below, the time to first token is Llama-PromptProcessor-Quantized's latency and average time per addition token is Llama-TokenGenerator-KVCache-Quantized's latency.

This is based on the implementation of Llama-v2-7B-Chat found
[here](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf). More details on model performance
accross various devices, can be found [here](https://aihub.qualcomm.com/models/llama_v2_7b_chat_quantized).

### Model Details

- **Model Type:** Text generation
- **Model Stats:**
  - Number of parameters: 7B
  - Model size: 3.6GB
  - Model-1 (Prompt Processor): Llama-PromptProcessor-Quantized
  - Max context length: 1024
  - Prompt processor input: 1024 tokens
  - Prompt processor output: 1024 output tokens + KVCache for token generator
  - Model-2 (Token Generator): Llama-TokenGenerator-KVCache-Quantized
  - Token generator input: 1 input token + past KVCache
  - Token generator output: 1 output token + KVCache for next iteration
  - Decoding length: 1024 (1 output token + 1023 from KVCache)
  - Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
  - QNN-SDK: 2.19


| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
| ---|---|---|---|---|---|---|---|
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 117.812 ms | 66 - 238 MB | UINT16 | NPU | Llama-TokenGenerator-KVCache-Quantized 
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 2578.521 ms | 12 - 17 MB | UINT16 | NPU | Llama-PromptProcessor-Quantized 


## License
- The license for the original implementation of Llama-v2-7B-Chat can be found
  [here](https://github.com/facebookresearch/llama/blob/main/LICENSE).

## References
* [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
* [Source Model Implementation](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)

## 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