Llama 3 Instruct 70B is a large-scale language model developed by Meta as part of the initial Llama 3 family release. It is an instruction-tuned model optimized for dialogue, reasoning, and code generation. The model was trained on a massive dataset of over 15 trillion tokens—a sevenfold increase compared to Llama 2—emphasizing improved performance in knowledge retrieval and logical consistency.
Technical Architecture
The model utilizes a decoder-only transformer architecture and incorporates Grouped Query Attention (GQA) to enhance inference efficiency and scalability. It features a updated tokenizer with a vocabulary size of 128,256 tokens, which improves tokenization efficiency for both English and certain code-related tasks. The instruction-tuning process involved a combination of Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), focusing on helpfulness and safety.
Capabilities
With 70 billion parameters, the model is designed to handle high-complexity tasks such as mathematical reasoning, nuanced conversation, and multi-step instructions. It supports a context window of 8,192 tokens. At release, Meta reported that Llama 3 Instruct 70B achieved significant gains on industry-standard benchmarks including MMLU, HumanEval, and GSM8K, making it one of the highest-performing open-weights models available at the time.