Free Guide: How to Fine-Tune and Prompt Engineer LLMs
While some of the most forward-thinking companies in the world are already using LLMs, few organizations have the bandwidth, compute, or money to train foundational models in-house. It’s become much more common to either fine-tune or prompt engineer existing LLMs for unique business needs. In this guide, you’ll learn:
• How to choose between fine-tuning and prompting
• Popular fine-tuning strategies and their trade-offs
• Tasks where fine-tuning excels vs. ones where it doesn’t
• Tips and current best practices for prompt engineering
• And a whole lot more!
Weights & Biases enables the collaboration required to produce these complex, expensive models and push them to production. We’re happy to showcase a few things we’ve learned along the way. The whitepaper is free and will be emailed to you via the form on the right.
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Trusted by the teams building state-of-the-art LLMs
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Research Engineer – Facebook AI Research
VP of Product- OpenAI
Product Manager- Cohere
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With Weights & Biases you can:
Overview
- Company size: 300+
- Industry: Autonomous vehicles
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Problem
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Solution
Instead of tinkering with brittle internal tools and ad-hoc solutions for experiment tracking and prediction visualizations, the ML team was able to standardize with Weights & Biases’ lightweight experiment tracking and visualization solutions.
The Weights & Biases dashboard gave machine learning practitioners a command center to compare across dataset and model versions, maintaining a reliable record of every experiment and result. ML engineers are now free to focus on the valuable work of model development, accelerating project progress.
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