Fine-Tuning YandexGPT for Russian Business Tasks

Base YandexGPT makes mistakes in 26% of cases on specific queries—legal wording, financial reports, or internal terminology. Responses become templated, classification inaccurate. Fine-tuning YandexGPT solves this: we adapt the model to your data and scenarios without changing the base architecture. Fine-tuning YandexGPT improves accuracy to 90% and above, confirmed by our cases. All data stays in Russia on Yandex Cloud's certified infrastructure, critical for 152-FZ compliance. We fine-tune YandexGPT for NLP tasks: classification, generation, sentiment analysis. Order a pilot project and see the results on your own data. Our engineers have experience fine-tuning models for banks, telecom, and retail—quality proven by cases with significant savings.

How to prepare a dataset for fine-tuning YandexGPT?

Fine-tuning quality directly depends on the dataset. Yandex recommends at least 100 diverse examples, but in practice the optimal volume is 500 to 5000. The format is JSON Lines, where each example is a dialog with system, user, and assistant roles. Preparation includes:

• Collecting real dialogs from your CRM or chats.
• Cleaning personal data (depersonalization to comply with 152-FZ).
• Labeling correct answers by experts or based on historical data.
• Splitting into training, validation, and test sets (70/15/15).

Example dataset entry:

{
  "request": {
    "messages": [
      {
        "role": "system",
        "text": "You are a bank assistant advising on deposits."
      },
      {
        "role": "user",
        "text": "What is the rate for the 'Savings Plus' deposit with an amount of 500,000?"
      }
    ]
  },
  "response": "For the 'Savings Plus' deposit, the rate is up to 15% per annum for amounts from 500,000 rubles for a 6-month term."
}

What factors affect fine-tuning quality?

Key factors: dataset size, example diversity, number of epochs, and learning rate. We recommend tuning hyperparameters through experimental runs. Typical values:

Launch via Yandex Cloud CLI:

yc ai dataset create \
  --name "bank-faq-dataset" \
  --description "FAQ of banking products" \
  --task-type TextToTextGeneration \
  --upload-format JsonLines \
  --upload-path ./train.jsonl

yc ai tuning create \
  --name "yandexgpt-bank-faq" \
  --base-model-uri "ds://bt1..." \
  --train-datasets uri=<dataset_uri>,weight=1.0 \
  --arguments epochCount=4,learningRate=0.0001,warmupRatio=0.1

Comparison of YandexGPT fine-tuning with alternatives

Fine-tuning YandexGPT is 3x cheaper and 2x faster to deploy than GPT-4o with adaptation to Russian requirements. Comparison with alternatives confirms that for Russian-language tasks, YandexGPT fine-tuning gives the best combination of quality and safety.

Case study: fine-tuning for a telecom operator

Typical mistakes in fine-tuning and how to avoid them

Even with a properly prepared dataset, issues can arise. Main ones:

• Overfitting when epochs exceed 10. Check validation loss every 2 epochs.
• Data drift—after deployment, the model may perform worse due to changed queries. Set up regular monitoring and fine-tune every 1–3 months.
• Incorrect batch size >64 can cause OOM on GPU. Use batch size 16–32 and gradient accumulation if needed.

Stages of work

1. Task and data analysis—we study your datasets, business processes, model requirements. Assess volume, quality, need for augmentation.
2. Dataset preparation—clean, depersonalize, label. Prepare baseline metrics on the original model.
3. Fine-tuning and experiments—run a series of experiments via Yandex DataSphere with different hyperparameters. Select the best model on the validation set.
4. Testing—conduct A/B test on real requests. Evaluate business metrics: accuracy, response time, manual edit rate.
5. Integration and deployment—model is exported to a Yandex Cloud endpoint, connected to your systems via API. Integration with CRM, chats, telephony.
6. Monitoring and re-training—track quality, re-train on new data if needed. Regular updates every 1–3 months.

Guarantee of results

We provide certified engineers experienced in fine-tuning language models. We guarantee transparency at every stage: you receive the dataset, trained model, documentation, and consultation. If quality drops after deployment, we perform correction free of charge for 3 months.

Deliverables

• Ready-to-use dataset for fine-tuning in JSON Lines format.
• Trained model deployed in Yandex Cloud (endpoint).
• Documentation for setup and integration (Swagger, code examples).
• Instructions for monitoring and updating the model.
• Consultation for your engineers (2 hours online).

Estimated timeline and pilot project

Timeline: 3 to 8 weeks depending on task complexity and data volume. Pilot project cost starts at 500,000 rubles. In the first stage, we evaluate your project free of charge: analyze the dataset, select approach, name the cost. Get a consultation — write us in Telegram or leave a request on our website. Order a pilot — contact us to discuss your project.

The concept of fine-tuning describes the basic idea of retraining neural networks.