Llama Chat Reviews:open source, free for research and commercial use
About Llama Chat
Llama 2 was pretrained on publicly available online data sources.
The fine-tuned model, Llama Chat, leverages publicly available instruction datasets and over 1 million human annotations.
We’re unlocking the power of these large language models. Our latest version of Llama – Llama 2 – is now accessible to individuals, creators, researchers, and businesses so they can experiment, innovate, and scale their ideas responsibly.
Available as part of the Llama 2 release
With each model download you’ll receive:
- Model code
- Model Weights
- README (User Guide)
- Responsible Use Guide
- Acceptable Use Policy
- Model Card
Llama 2 was pretrained on publicly available online data sources. The fine-tuned model, Llama-2-chat, leverages publicly available instruction datasets and over 1 million human annotations.
Inside the model
Llama 2 models are trained on 2 trillion tokens and have double the context length of Llama 1. Llama-2-chat models have additionally been trained on over 1 million new human annotations.
Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests.
Llama-2-chat uses reinforcement learning from human feedback to ensure safety and helpfulness.
Training Llama-2-chat: Llama 2 is pretrained using publicly available online data. An initial version of Llama-2-chat is then created through the use of supervised fine-tuning. Next, Llama-2-chat is iteratively refined using Reinforcement Learning from Human Feedback (RLHF), which includes rejection sampling and proximal policy optimization (PPO).
Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.
Like all LLMs, Llama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios. In order to help developers address these risks, we have created the Responsible Use Guide. More details can be found in the Research paper and Model card.
The Responsible Use Guide is a resource for developers that provides best practices and considerations for building products powered by large language models (LLM) in a responsible manner, covering various stages of development from inception to deployment.
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