Model Selection

You can currently fine-tune the following models on Texti :

  1. davinci-002 - Davinci is the most capable model, excelling in deep understanding of content, complex intent, cause and effect, and summarization for specific audiences. It requires more compute resources and is slower compared to other models.

  2. babbage-002 - Babbage is suitable for straightforward tasks like simple classification. It performs well in semantic search, ranking how well documents match search queries.

  3. LlaMa-65B (limited availability) - LlaMa-65B is a more efficient model, part of a series emphasizing reduced compute demands. These models facilitate versatile fine-tuning and are trained on vast data sets, including texts from the top 20 spoken languages

  4. BERT - BERT revolutionized NLP, capturing context in both text directions. It's adaptable across domains but demands more data for fine-tuning.

While cost and speed are important considerations, the availability of data can also significantly influence the decision-making process when selecting the appropriate model.

Data Availability & Model Choice

If you have access to a large amount of data, you can effectively fine-tune a less powerful, less expensive OpenAI base model and achieve satisfactory results. However, in cases where you have limited data, leveraging a more powerful and expensive OpenAI base model can be advantageous.

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