Fine-Tuning GigaChat for Corporate AI Assistants

We often see projects where the base GigaChat produces generic answers to corporate queries. Fine-tuning solves this: the model learns internal regulations, terminology, and context. But the process requires deep understanding of Sber's ecosystem architecture and constraints. For example, an insurance company asked us to adapt an assistant to their product line. The base model confused CASCO and OSAGO terms, causing recommendation errors. After fine-tuning, answer accuracy reached 95%. However, this required careful preparation: labeling 1,200 dialogs, balancing topics, and three training iterations. We use LoRA adapters to save resources—this reduces GPU-hour costs by 2–3x compared to full fine-tuning. The project budget is calculated individually based on data volume.

According to Sber Cloud documentation, fine-tuning allows adapting the model to a specific domain.

Let's walk through a banking assistant example: how to prepare data, run training, and integrate the fine-tuned model. Our engineers have 10+ years of experience in NLP and MLOps, guaranteeing results.

Why Fine-Tune GigaChat for Corporate Tasks?

The standard model gives generic answers. Fine-tuning addresses three key issues: contextual accuracy (the model absorbs internal regulations and terms, critical for banks with regulatory frameworks), reduced hallucinations (training on your data decreases fabricated facts—we've observed error rates drop by 30–50%), and security (all data stays within Sber Cloud, compliant with Federal Law 152-FZ for banks and government agencies).

Compare with alternatives: YandexGPT also offers corporate fine-tuning, but GigaChat integrates deeper with Sber's ecosystem. Llama self-hosted requires its own infrastructure but is vendor-independent.

How to Prepare Data for GigaChat Fine-Tuning?

GigaChat API is available via the Sber Cloud platform. Fine-tuning requires a corporate contract and Enterprise plan. Authentication uses OAuth 2.0:

import requests
import base64

credentials = base64.b64encode(
    f"{client_id}:{client_secret}".encode()
).decode()

response = requests.post(
    "https://ngw.devices.sberbank.ru:9443/api/v2/oauth",
    headers={
        "Authorization": f"Basic {credentials}",
        "RqUID": "unique-request-id",
    },
    data={"scope": "GIGACHAT_API_CORP"}
)
access_token = response.json()["access_token"]

The obtained token is used for all subsequent requests. For training, you must upload a dataset in JSON Lines format, where each example is a dialog with system, user, assistant roles:

{
  "messages": [
    {
      "role": "system",
      "content": "You are an insurance company assistant. Help clients understand insurance product terms."
    },
    {
      "role": "user",
      "content": "What does CASCO insurance cover in case of an accident caused by a third party?"
    },
    {
      "role": "assistant",
      "content": "In an accident caused by a third party, CASCO policy covers: collision damage regardless of fault, repair costs..."
    }
  ]
}

For quality training, at least 1,000 dialogs are needed, preferably 3,000+. It is essential to depersonalize data, balance topics (no more than 15% per category), and label complex cases.

Practical Example: Assistant for a Banking Chatbot

Task: fine-tune GigaChat Pro to handle incoming inquiries for a retail bank—answer product questions, route complex requests to operators.

Dataset: 3,500 dialogs from real (anonymized) correspondence, covering 45 topics (loans, deposits, cards, transfers, transaction disputes).

Preparation steps:

1. Extract dialogs from CRM.
2. Depersonalize.
3. Filter dialogs with negative outcomes.
4. Label complex cases.
5. Balance by topics.

Results:

• CSAT (customer satisfaction with bot response): from 3.2 to 4.1 out of 5.
• Correct routing rate: from 71% to 94%.
• Escalation rate dropped: from 61% to 38%.
• Average dialog time decreased by 22%.

What Limitations to Consider?

Closed weights: like GPT-4o, you get a hosted endpoint without access to weights. Infrastructure lock-in: only Sber Cloud, no on-premise deployment. Corporate barrier: fine-tuning unavailable on free plans. Context size: 32K tokens—less than Qwen2.5 or Claude 3.5 Sonnet.

Comparison with Related Solutions

What the Project Includes

We offer a full cycle of work:

• Task audit—evaluate data, define metrics, determine training plan.
• Dataset preparation—depersonalization, labeling, balancing.
• Fine-tuning execution—iterative training with metric monitoring.
• Testing—A/B testing on real dialogs.
• Integration and support—API endpoint setup, documentation, operator training.

We guarantee quality and provide certificates for completed work. Order a free audit of your data—we will assess fine-tuning feasibility in 2 days. The project budget is calculated individually. Contact us for a consultation.

Project Timeline