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This is the basic reasoning behind the Fine-Tuning Argument for God's existence. We can summarize the argument as follows: If God does not exist, then it was extremely unlikely that the universe would permit life. But if God exists, then it was very likely that the universe would permit life.

The argument from fine-tuning for design as reviewed in Section 3.1 treats the fact that life requires fine-tuned conditions as background knowledge and assesses the evidential significance of the observation that life-friendly conditions obtain against that background.

Three steps to fine-tune LLMs using data labeling: ChatGPT's example Step 1: Supervised Fine Tuning (SFT) SFT involves a team of human annotators creating a set of prompts and their expected outputs. ... Step 2: Reward Model. The reward model overcomes the SFT's ability to scale. ... Step 3: Proximal Policy Optimization (PPO)

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.

Preprocess data Next, we need to preprocess our data so that it can be used for training. This consists of using a izer to convert the text into an integer representation understood by the base model. To apply the izer to the dataset, we use the .map() method.

Ensure the examples are of high quality and follow the same desired format. Ensure that the dataset used for fine-tuning is similar in structure and type of task as what the model will be used for. Using Lower learning rate and only 1-2 epochs tends to work better for these 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).

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.