Hugging Face's logo

qualcomm
/
Llama-v2-7B-Chat

Text Generation
PyTorch
llm
generative_ai
quantized
android
Model card Files Community
8
Llama-v2-7B-Chat / README.md
shreyajn's picture
shreyajn
Upload README.md with huggingface_hub
91cc4a9 verified
preview code
|
raw
history blame contribute delete
6.01 kB
metadata
library_name: pytorch
license: llama2
pipeline_tag: text-generation
tags:
  - llm
  - generative_ai
  - quantized
  - android

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 accross 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 QCS8550 (Proxy) QCS8550 Proxy 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
  1. --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.
  1. --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

Community

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