Resource Intensity
Traditional fine-tuning requires significant computational resources and time
LoRA fine-tuning for LLMs and diffusion models
We train, merge and deploy Low-Rank Adaptation (LoRA) models for your use case.
Talk to a founder →Related things we’ve built.
- NTC Slider Factory — lets anyone create text-to-image LoRAs from words alone, with no dataset; our NTCAI models have over 5 million uses and downloads.
- ConceptMod — our open source tool that NTC Slider Factory is built on.
Challenges in AI Model Fine-Tuning
Overfitting Risk
Fine-tuning entire models can lead to overfitting and loss of general capabilities
Deployment Complexity
Managing and deploying large fine-tuned models presents logistical challenges
A scope we can shape together.
These are starting points for a conversation. We’ll agree the scope and timing around your project.
Domain-Specific Adaptation
- Image styling
- Legal Text Processing
- Medical Literature Analysis
- Financial Report Generation
Typical Timeline: 4-6 weeks
Task-Specific Fine-Tuning
- Sentiment Analysis Refinement
- Custom Named Entity Recognition
- Specialized Question Answering
Typical Timeline: 3-5 weeks
Multilingual Adaptations
- Language-Specific Fine-Tuning
- Cross-Lingual Transfer Learning
- Low-Resource Language Adaptation
Typical Timeline: 5-8 weeks
Technologies we use.
The problem decides the approach. These are some of the tools we reach for in this kind of work.
frameworks
- PyTorch
- Hugging Face Transformers
- PEFT (Parameter-Efficient Fine-Tuning)
models
- GPT-4
- Diffusion Models
- Llama
Talk to us about fine-tuning with LoRAs
Email a few lines about what you’re building, what you’d like to improve, or where you’re stuck. Martyn or Mikkel will reply personally.
Or reach a founder directly: Martyn Garcia · Mikkel Garcia