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Fine-tuning Procedure Pre-training. Pre-training is the first step in the process of adjusting huge language models. ... Task-relevant layers. The next stage in fine-tuning a large language model is to add task-specific layers after pre-training. ... Data preparation. ... Fine-tuning. ... Iteration and evaluation.

We can fine tune an LLM that's comparable to #ChatGPT with just 1,000 samples in the fine tuning! A dataset of 1000 samples is sourced from sources like WikiHow, StackExchange, and Reddit by ensuring prompt diversity, quality, and particular answering style.

The consensus is that fine tuning LLMs works reasonably for smaller scale instruction tuning, where you pass in ~1k-10k input/output examples to modify the model output.

OpenAI has launched fine-tuning capabilities for its GPT-3.5 Turbo large language model API, with plans to do the same with GPT-4 this fall. The fine-tuning will allow developers to customize the generative AI model to perform better on specific use cases.

Supervised Fine-tuning Steps (High-level) Choose fine-tuning task (e.g. summarization, question answering, text classification) Prepare training dataset i.e. create (100?10k) input-output pairs and preprocess data (i.e. ize, truncate, and pad text).

For example, a gpt-3.5-turbo fine-tuning job with a training file of 100,000 s that is trained for 3 epochs would have an expected cost of $2.40.

The more training examples you have, the better. We recommend having at least a couple hundred examples. In general, we've found that each doubling of the dataset size leads to a linear increase in model quality. The first part of finetuning is to select the data you wish to finetune on.