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

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/llama_v2_7b_chat/web-assets/model_demo.png)

# Llama-v2-7B-Chat: Optimized for Qualcomm Devices

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 w4a16(4-bit weights and 16-bit activations) and part of the model is quantized to w8a16(8-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).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/llama_v2_7b_chat) 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 Llama 2 on-device

Please follow the [LLM on-device deployment](https://github.com/qualcomm/ai-hub-apps/tree/main/tutorials/llm_on_genie) tutorial.

## Sample output prompts generated on-device
1. --prompt "what is gravity?" --max-output-tokens 30
~~~
-------- Response Summary --------
Prompt: what is gravity?
Response: Hello! I'm here to help you answer your question. Gravity is a fundamental force of nature that affects the behavior of objects with mass
~~~

2. --prompt "what is 2+3?" --max-output-tokens 30
~~~
-------- Response Summary --------
Prompt: what is 2+3?
Response: Of course! I'm happy to help! The answer to 2+3 is 5.
~~~

3. --prompt "could you please write code for fibonacci series in python?" --max-output-tokens 100
~~~
-------- Response Summary --------
Prompt: could you please write code for fibonacci series in python?
Response: Of course! Here is an example of how you could implement the Fibonacci sequence in Python:
```
def fibonacci(n):
    if n <= 1:
        return n
    else:
        return fibonacci(n-1) + fibonacci(n-2)
```
You can test the function by calling it with different values of `n`, like this:
```
print(fibonacci(5))
~~~

## Getting Started
Due to licensing restrictions, we cannot distribute pre-exported model assets for this model.
Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/llama_v2_7b_chat) 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

See our repository for [Llama-v2-7B-Chat on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/llama_v2_7b_chat) for usage instructions.


## Model Details

**Model Type:** Model_use_case.text_generation

**Model Stats:**
- Input sequence length for Prompt Processor: 1024
- Context length: 1024
- Quantization Type: w4a16 + w8a16 (few layers)
- Supported languages: English.
- 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. For Llama-v2-7B-Chat, both values in the range are the same since prompt length is the full context length (1024 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)
|---|---|---|---|---|---|---
| Llama-v2-7B-Chat | QNN_CONTEXT_BINARY | w4a16 | Snapdragon® 8 Elite Mobile | 1024 | 17.94 | 1.44 - 1.44
| Llama-v2-7B-Chat | QNN_CONTEXT_BINARY | w4a16 | Snapdragon® X Elite | 1024 | 11.2 | 1.919 - 1.919
| Llama-v2-7B-Chat | QNN_CONTEXT_BINARY | w4a16 | Snapdragon® 8 Gen 3 Mobile | 1024 | 12.85 | 1.49583 - 1.49583
| Llama-v2-7B-Chat | QNN_CONTEXT_BINARY | w4a16 | Qualcomm® QCS8750 | 1024 | 17.94 | 1.44 - 1.44
| Llama-v2-7B-Chat | QNN_CONTEXT_BINARY | w4a16 | Qualcomm® QCS7181 | 1024 | 11.2 | 1.919 - 1.919

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