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Training · Reinforcement Learning · Safety

Llama 2

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First page
Llama 2
The curator’s take

Meta's open-weight foundation model family with chat-tuned variants ranging from 7B to 70B parameters.

Key points
01

Open-weight release: Released pretrained and RLHF-tuned chat models under a permissive license that allowed commercial use, reshaping the open-source LLM landscape.

02

Training recipe: Pretrained on 2T tokens with 4K context; chat models use SFT followed by iterative RLHF with Ghost Attention (GAtt) for multi-turn consistency.

03

Safety investment: Extensive red-teaming, safety reward models, and context distillation produce chat models with strong helpfulness-safety trade-offs.

04

Ecosystem catalyst: Llama 2 became the base for hundreds of community fine-tunes (Vicuna, WizardLM, CodeLlama) and catalyzed the open-weight movement that 2024's Llama 3 and Mistral would extend.

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