LLM fine-tuning services
We fine-tune Large Language Models (LLMs) to enhance their performance and relevance for domain-specific tasks. We use advanced techniques to ensure your models achieve desired accuracy.
Our Generative AI development expertise
We develop and deploy LLMs in your private infrastructure
Advanced LLM fine-tuning techniques we use
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1. Supervised fine-tuning
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2. Basic hyperparameter tuning
We optimize critical hyperparameters – such as learning rate, batch size, and number of epochs – through systematic experimentation. This fine-tuning enhances model performance without requiring extensive retraining.
How we approach it:
- Experimenting with different hyperparameter combinations to find the ideal configuration
- Tuning learning rates for improved convergence speed and model stability
- Tracking performance metrics to measure the model’s stability and accuracy improvements
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3. Multi-task learning
Multi-task learning enhances LLM to handle multiple, related tasks simultaneously. It improves adaptability and task performance by sharing knowledge across domains.
Here’s how it works:
- Identifying complementary tasks like summarization and translation to leverage cross-task learning
- Using shared training data to help the model learn from multiple tasks at once
- Evaluating multi-task outcomes to ensure better performance across diverse applications (e.g. question answering and generation)
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4. Few-shot learning
We leverage few-shot learning to fine-tune LLMs using minimal amounts of data. This method allows models to generalize effectively even when provided with limited examples.
Our approach involves:
- Pinpointing tasks that require fewer data but deliver impactful results
- Training the model with limited data while ensuring generalization and accuracy
- Validating model performance in scenarios where labeled data is sparse
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5. Task-specific fine-tuning
For domain-specific challenges, we perform task-specific fine-tuning to ensure that the model achieves peak performance for well-defined, specialized tasks like financial forecasting or legal document analysis.
We focus on:
- Understanding the unique requirements of your industry and tasks
- Customizing the model to fine-tune it on task-specific datasets for maximum accuracy
- Evaluating results to verify that the model meets the expected performance standards
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6. Reinforcement Learning from Human Feedback (RLHF)
We implement Reinforcement Learning from Human Feedback (RLHF) to continuously refine the model’s outputs based on human feedback loops. It improves the alignment of the model’s responses with real-world user expectations.
Our method entails:
- Setting up human feedback systems to provide real-time evaluations of model outputs
- Applying reinforcement learning techniques to adjust the model’s decision-making based on this feedback
- Iteratively improving the model by refining its behavior and performance with ongoing input
They trusted our expertise
Why?
Benefits of LLM fine-tuning
Customization for industry-specific tasks
Tailoring LLMs to industry-specific tasks results in better performance on specialized challenges, such as legal document analysis, medical diagnostics, or financial forecasting.
Bias mitigation
Curating training datasets that reflect diverse perspectives, we can create models that generate more balanced and ethical outputs. It reduces the risk of controversial or biased content.
Reduced (training) costs and time
Fine-tuning uses the foundational knowledge gained during pre-training. It means less time and fewer resources are required compared to developing a new model from scratch.
High-Performance LLMs
Core components of effective LLM fine-tuning
Our featured Gen AI projects leveraging fine-tuning
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LLM-powered Contract Analysis
Fine-tuned legal GenAI for risk analysis and contract compliance
- Step-by-step contract processing: Upload contracts in formats like DOCX or PDF. The system organizes and categorizes them automatically for easier document management.
- Automated risk and compliance analysis: AI automatically extracts key information, generates a summary, and provides a detailed list of risks and recommendations based on the organization’s knowledge base.
- Legal chatbot assistance: Ask questions about specific sections or compliance issues through an AI chatbot. It provides precise, context-aware answers based on the fine-tuned model and knowledge base.
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AI Agent
AI-powered assistant for customer service interactions
CLIENT: CREDIT AGRICOLE
- Message understanding: The system extracts key information from incoming messages and generates a summary containing the purpose and emotional tone. It helps eliminate human errors and ensures clear and uniform language
- Intelligent routing: Simple requests are handled automatically for faster resolution, freeing up agents for more complex and personal interactions. More complicated messages are passed to the right teams.
- Generating resources: The system creates customized draft replies and snippets. It can format them into PDFs for sending. It helps improve customer satisfaction scores, and meet service-level agreements.
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AI assistant
Intelligent sales assistant for credit card recommendations
CLIENT: BANK • UAE
- Meeting preparation assistance: The assistant helps sales representatives prepare for customer meetings. It provides detailed reminders about product terms and benefits for accurate and personalized recommendations.
- Real-time data analysis: The assistant analyzes input from the salesperson in real-time and compares it against the conditions of over 20 different credit card products. Then, it issues accurate recommendations that meet both client expectations and bank requirements.
- Integration with up-to-date product data: Direct integration with the bank’s product database ensures recommendations are based on the latest offer conditions.
We build safe, compliant, and ethical AI systems
Security & ethics in AI
LLM safety guidelines
We implement clear safety guidelines for the responsible use of LLMs. These guidelines help ensure that the fine-tuned models operate within safe boundaries, minimizing risks related to inappropriate or biased outputs.
Acceptable AI use policies
We help develop tailored AI use policies that align with your organization’s ethical standards and business goals. These policies govern how LLMs are deployed and used across your operations, ensuring that they meet both legal and ethical requirements.
Ethical LLM practices
Our LLM fine-tuning process adheres to ethical principles of fairness, transparency, and accountability. We ensure that the models we fine-tune are not only effective but also compliant with ethical standards relevant to your industry.
Testimonial
What our clients say
By automating certain customer interactions, bank employees are provided with a prepared “semi-product”, which enables them to dedicate more time to personalizing and empathizing with customer communication, and thus taking even better care of their needs.
Why choose us
LLM fine-tuning experts
Advanced fine-tuning techniques
Industry standards compliance
Domain expertise
Get in touch
Let’s talk
Book 1-on-1 consultation
Grzegorz Motriuk
Head of Sales | Application Development
Our consultant is at your disposal from 9 AM to 5 PM CET working days from Monday to Friday for any additional questions.
FAQ
Common questions regarding LLM fine-tuning
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How long does it take to fine-tune an LLM?
The timeframe for fine-tuning depends on the model size, complexity, and data volume. Typically, the process takes a few days to a couple of weeks, depending on specific requirements. We ensure efficient fine-tuning for both custom and pre-trained models.
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What data do we need to provide for fine-tuning?
We require domain-specific labeled data that aligns with the tasks you want the LLM to perform. This can include customer interactions, financial reports, or industry-specific documents. If necessary, we offer data labeling and preparation services to ensure optimal results.
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How do you ensure data security during LLM fine-tuning?
We prioritize data security by offering self-hosted fine-tuning within your private infrastructure. All data is encrypted both in transit and at rest, adhering to standards like GDPR and HIPAA for complete regulatory compliance.
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Can fine-tuning be done in a regulated industry like finance or healthcare?
Yes, we specialize in fine-tuning LLMs for regulated industries. We ensure full compliance with industry-specific regulation. This enables LLMs to handle sensitive financial data or healthcare records securely and in accordance with legal requirements.