![]() Finally, we cleaned up formatting by converting HTML to Markdown to make the model’s outputs more readable. We sample at most ten answer pairs per question to limit the number of data points per question. Some questions have dozens of answers, leading to many possible pairs. Score = log2 (1 + upvotes) rounded to the nearest integer, plus 1 if the questioner accepted the answer (we assign a score of −1 if the number of upvotes is negative).įor the reward model, we will always need two answers per question to compare, as we’ll see later. We follow the approach described in Askell et al. It is attractive for this use case because the answers come together with the number of upvotes and a label for the accepted answer. The dataset includes questions and their corresponding answers from the StackExchange platform (including StackOverflow for code and many other topics). In order to bootstrap the process for this example while still building a useful model, we make use of the StackExchange dataset. Gathering human feedback is a complex and expensive endeavor. 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.
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