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 Llama-v3-8B-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: 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
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