Model tuning
With Yandex Foundation Models, you can tune YandexGPT Lite and Llama 8B1 text generation models and YandexGPT Lite-based classifiers using the LoRA (Low-Rank Adaptation of Large Language Models) method.
Model tuning in Yandex Foundation Models is at the Preview stage.
Fine-tuning text generation models
You cannot fine-tune a text generation model based on new data, e.g., the knowledge base of your support service. However, you can train the model to generate responses in a specific format or analyze texts. You can train the model to:
- Summarize and rewrite texts.
- Generate questions and answers from text input.
- Provide responses in a particular format or style.
- Classify texts, queries, and conversations.
- Extract entities from texts.
Fine-tuning in Foundation Models
For more information on tuning data requirements, see Fine-tuning text generation models and Tuning classification models.
You need to upload the prepared data to Yandex Cloud as a dataset. By default, you can upload up to 5 GB of tuning data into one dataset. For all limitations, see Quotas and limits in Yandex Foundation Models.
After you upload a dataset, start tuning by specifying its type and parameters (optional). Tuning can take from 1 to 24 hours depending on the amount of data and system workload.
For a model tuning example, see Creating a fine-tuned text generation model in Foundation Models.
You will need an ai.editor role for model tuning in Foundation Models. This role allows you to upload data and start the tuning process.
Examples
Creating a fine-tuned text generation model in Foundation Models.
1 Llama was created by Meta. Meta is designated as an extremist organization and its activities are prohibited in Russia.