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 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 model is an implementation of Llama-v2-7B-Chat found here.

More details on model performance across various devices, can be found here.

Model Details

• Model Type: Text generation
• Model Stats:
  • Input sequence length for Prompt Processor: 1024
  • Context length: 1024
  • Number of parameters: 7B
  • Precision: w4a16 + w8a16 (few layers)
  • Model-1 (Prompt Processor): Llama-PromptProcessor-Quantized
  • Prompt processor model size: 3.6 GB
  • 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 model size: 3.6 GB
  • Token generator input: 1 input token + past KVCache
  • Token generator output: 1 output token + KVCache for next iteration
  • Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
  • Minimum QNN SDK version required: 2.27.0
  • 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.

Model	Device	Chipset	Target Runtime	Response Rate (tokens per second)	Time To First Token (range, seconds)
Llama-v2-7B-Chat	Samsung Galaxy S24	Snapdragon® 8 Gen 3	QNN	12.85	1.49583 - 1.49583
Llama-v2-7B-Chat	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	11.2	1.9189999999999998 - 1.9189999999999998
Llama-v2-7B-Chat	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	17.94	1.44 - 1.44

Deploying Llama 2 on-device

Please follow the LLM on-device deployment 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))

License

• The license for the original implementation of Llama-v2-7B-Chat can be found here.
• The license for the compiled assets for on-device deployment can be found here

References

• LLaMA: Open and Efficient Foundation Language Models
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us.

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