Streamlining Large Language Models: Benefits of Fine-Tuning for Business Tasks

Unlocking the power of fine-tuning large language models without code, enabling businesses to streamline tasks like writing, scrubbing fake emails, and detecting fraud with ease and unparalleled efficiency.

1. Mark Hennings is a serial entrepreneur and will discuss fine-tuning large language models without code.
2. Fine-tuning involves training a foundation model for a specialized task, such as writing copy, scrubbing emails, normalizing data, translating text, paraphrasing, rewriting, qualifying sales leads,
3. Traditional programming or rule-based approaches often do not work well for these tasks, but large language models perform them with ease and can capture nuances in text.
4. Fine-tuning has advantages over prompt engineering:
* It is faster and cheaper, as a lighter model can match the quality of a prompt's output, reducing the size of prompts for longer completions.
* Training examples allow for better collaboration within teams and help cover edge cases.
* Fine-tuned models are naturally resistant to prompt injection attacks.
5. GPT-4 takes about 196 milliseconds per token in response time, while GPT-3.5 takes around 73 milliseconds—a difference of three times faster.
6. Fine-tuning can save up to 88.6% of the cost compared to using a larger model like GPT-4 versus a fine-tuned GPT-3.5 model.
7. Prompts can be significantly shortened with fine-tuning, as models learn how to write based on training examples rather than needing detailed instructions for each task.
8. Fine-tuning enables better team collaboration by allowing multiple people to work on different sections of the training data, which feeds into the fine-tune model.
9. Fine-tuning is currently considered a Dev job, but with proper tooling, it can be made more accessible.
10. To get started with fine-tuning, you need at least 20 examples of what you want your model to do; this is much simpler than traditional machine learning requiring thousands of examples.
11. Fine-tuning can be thought of as an extension to few-shot learning, where a larger training example dataset helps the model perform more accurately.
12. A proposed Dev lifecycle for large language models involves:
* Prompt engineering for prototyping and initial data set creation
* Fine-tuning a model using the created data sets
* Evaluating the fine-tuned model to ensure it outperforms the prompt engineered version
* Testing which models can perform at the same level as the finetune model
* Continuously improving the fine-tune model with real-world examples captured from production
13. Roles in prompt engineering and fine tuning are not limited to developers, as non-developers can also contribute.
14. Entrypoint, a company co-founded by Mark Hennings, provides modern tooling to make fine-tuning more accessible.
15. With Entrypoint's dashboard, users can open projects and see their data sets, such as the press release writer project example provided.
16. Users can create examples by finding relevant content online and using language models like GPT-4 to help generate input data.
17. Entrypoint uses structured data, with each column in a CSV becoming a field that can be used in templates for formatting output.
18. The fine-tuning process involves selecting the model, platform, and estimating cost before starting the training process.
19. After fine-tuning, users can generate outputs using their finetune models with the Entrypoint playground.
20. Fine-tuning allows for an iterative workflow of generating content, refining facts, and improving results.
21. Entrypoint offers additional features like data synthesis and tools to compare the performance of fine-tuned models.
22. Visit [entrypoint.com](http://entrypoint.com) to learn more and try out their platform.
23. Fine-tuning large language models can save time, money, and improve collaboration within teams.
24. Non-developers can contribute to prompt engineering and fine-tuning, expanding opportunities in this field.

Source: AI Engineer via YouTube

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