![]() To access the model, use the form from Meta AI. We use the 7B model as the base for all the following steps! They come in sizes ranging from 7B to 65B parameters and were trained on between 1T and 1.4T tokens, making them very capable. The LLaMA models are the latest large language models developed by Meta AI. Therefore, we choose to use the recently introduced and performant LLaMA models. When doing RLHF, it is important to start with a capable model: the RLHF step is only a fine-tuning step to align the model with how we want to interact with it and how we expect it to respond. To give you a taste of what the model can do, try out the demo below! This model is available on the □ Hub (see Meta's LLaMA release for the original LLaMA model) and the entire training pipeline is available as part of the Hugging Face TRL library. ![]() ![]() "Training language models to follow instructions with human feedback." arXiv preprint arXiv:2203.02155 (2022).īy combining these approaches, we are releasing the StackLLaMA model. Reinforcement Learning from Human Feedback (RLHF)įrom InstructGPT paper: Ouyang, Long, et al.In this blog post, we show all the steps involved in training a LlaMa model to answer questions on Stack Exchange with RLHF through a combination of: Models such as ChatGPT, GPT-4, and Claude are powerful language models that have been fine-tuned using a method called Reinforcement Learning from Human Feedback (RLHF) to be better aligned with how we expect them to behave and would like to use them.
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