LLM Fine Tuning
Services

Customize large language models to excel at your specific tasks and domain expertise

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Our services

We fine-tune state-of-the-art language models like GPT, Claude, and Llama on your data to create specialized AI that understands your business context and performs optimally for your use cases

Domain-Specific Fine Tuning

Adapt general-purpose LLMs to excel in specific domains like healthcare, finance, legal, or technical fields

Task-Specific Optimization

Fine-tune models for specific tasks like classification, summarization, code generation, or customer support

Brand Voice & Style Training

Train models to match your organization's specific communication style, tone, and brand voice

Multilingual Model Adaptation

Fine-tune models for improved performance in specific languages or multilingual scenarios

Efficiency & Speed Optimization

Optimize models for faster inference, reduced computational costs, and deployment on specific hardware

Privacy-Preserving Fine Tuning

Implement federated learning and differential privacy techniques for sensitive data applications


Why choose Aloa

250+

Clients Served

We've successfully delivered AI solutions to over 250 clients across diverse industries

82%

Client Referral

82% of our business comes from referrals - a testament to our exceptional service and results

8

Years in Business

8 years of proven expertise in AI development and digital transformation


Our development process

01
Data Collection & Preparation
Gather and curate high-quality training data specific to your domain and use case requirements
02
Model Selection & Architecture Design
Choose the optimal base model and design the fine-tuning approach for maximum performance
03
Training & Validation
Execute the fine-tuning process with rigorous validation and hyperparameter optimization
04
Evaluation & Deployment
Thoroughly evaluate model performance and deploy with monitoring and continuous improvement

Technologies we work with (just to name a few)

Base Language Models

GPT-4 GPT-4 OpenAI's flagship model
Claude 3 Claude 3 Anthropic's advanced LLM
Llama 3 Llama 3 Meta's open source model
Gemini Pro Gemini Pro Google's multimodal LLM
Mistral Mistral Efficient European model

Fine-Tuning Frameworks

Hugging Face Hugging Face Transformers library
LangChain LangChain LLM orchestration
PyTorch PyTorch Deep learning framework
TensorFlow TensorFlow ML platform
MLflow MLflow ML lifecycle management

Industries we serve


Frequently asked questions

What's the difference between fine-tuning and prompt engineering?

Prompt engineering modifies inputs to get better outputs from existing models, while fine-tuning actually modifies the model's parameters to learn new behaviors. Fine-tuning provides deeper customization and better performance for specific tasks, especially with domain-specific knowledge.

How much data do you need for effective fine-tuning?

The amount varies by use case, but typically we need 1,000-10,000 high-quality examples for task-specific fine-tuning, and more for domain adaptation. We can also use techniques like few-shot learning and data augmentation to work with smaller datasets.

Can you fine-tune models while keeping our data private?

Yes, we offer several privacy-preserving approaches including on-premises fine-tuning, federated learning, and differential privacy techniques. We can ensure your sensitive data never leaves your environment while still achieving excellent model performance.

How do you measure the success of fine-tuning?

We use comprehensive evaluation metrics specific to your use case, including accuracy, precision, recall, and domain-specific benchmarks. We also conduct human evaluations and A/B testing to ensure the fine-tuned model performs better than alternatives.

What happens if we need to update the model with new data?

We design fine-tuning pipelines for continuous learning. You can regularly update models with new data, and we provide tools for monitoring model drift and performance degradation. We also offer incremental fine-tuning to incorporate new information efficiently.

Can fine-tuned models work with existing AI systems?

Absolutely. Our fine-tuned models maintain compatibility with standard APIs and can be integrated into existing systems seamlessly. We provide the same interfaces as popular LLM APIs, making adoption straightforward for your development team.

Flexible engagement models

Basic Fine-Tuning

Task-specific model optimization

  • Single task optimization
  • 4-6 week implementation
  • Standard model selection
  • Performance evaluation
  • Basic deployment support
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Advanced Domain Adaptation

Comprehensive domain-specific fine-tuning

  • Multi-task optimization
  • Custom data preparation
  • Advanced model architectures
  • Extensive performance testing
  • Production deployment
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Enterprise LLM Platform

Complete custom language model solution

  • Multiple specialized models
  • Continuous learning pipeline
  • Advanced security measures
  • Scalable infrastructure
  • Dedicated ML engineering team
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Ready to Create Your Custom Language Model?

Let's discuss how fine-tuning can create AI that truly understands your business and excels at your specific tasks