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 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.
Input sequence length for Prompt Processor:128
Context length:4096
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
Language(s) supported: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.
Dialogue
Content Generation
Customer Support
Source Model: LLAMA3
Deployable Model: LLAMA3