README.md

metadata

library_name: pytorch
license: llama3
pipeline_tag: text-generation
tags:
  - llm
  - generative_ai
  - quantized
  - android

Llama-v3-8B-Chat: Optimized for Mobile Deployment

State-of-the-art large language model useful on a variety of language understanding and generation tasks

Llama 3 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-Quantized's latency.

This model is an implementation of Posenet-Mobilenet 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: 128
  • Context length: 4096
  • Number of parameters: 8B
  • Model size: 4.8GB
  • Precision: w4a16 + w8a16 (few layers)
  • Num of key-value heads: 8
  • Model-1 (Prompt Processor): Llama-PromptProcessor-Quantized
  • Prompt processor input: 128 tokens + position embeddings + attention mask + KV cache inputs
  • Prompt processor output: 128 output tokens + KV cache outputs
  • Model-2 (Token Generator): Llama-TokenGenerator-Quantized
  • Token generator input: 1 input token + position embeddings + attention mask + KV cache inputs
  • Token generator output: 1 output token + KV cache outputs
  • Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
  • Minimum QNN SDK version required: 2.27.7
  • 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. 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.

Model	Device	Chipset	Target Runtime	Response Rate (tokens per second)	Time To First Token (range, seconds)
Llama-v3-8B-Chat	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	12.9262	0.159383 - 5.100256
Llama-v3-8B-Chat	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	10.0367	0.211644 - 6.772608

Deploying Llama 3 on-device

Please follow the LLM on-device deployment tutorial.

License

• The license for the original implementation of Llama-v3-8B-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