Easily and Efficiently Fine-tune High Quality SLMs
Train LoRA adapters tailored for your use case with GPT-4 level performance—at a fraction of the cost.
Efficient and Effortless Fine-Tuning
- Intuitive UI
Start fine-tuning in the UI with best practice defaults, no code required.
- SDK
Advanced users can customize 100s of training parameters via the UI or SDK to optimize model performance.
Start fine-tuning in the UI with best practice defaults, no code required.
Fine-Tune Any Leading Model
Choose the base model that's best for your use case from a wide selection of LLMs including Upstage's Solar LLM and open-source LLMs like Llama-3, Phi-3, and Zephyr. You can also bring your own model and serve it as a dedicated deployment.
See the full list of supported models.
First-Class Fine-Tuning Experience
Powerful training engines
Fine-tuning uses A100s by default but you can choose other hardware to further optimize for cost or speed.
Serverless fine-tuning infra
Pay per token to ensure you’re only charged for what you use. See our pricing.
View essential metrics as you train
Track learning curves in real time as your adapter trains to ensure everything is on track.
Resume training from checkpoints
No need to restart an entire training job from the beginning if it encounters an error or you’re not happy with the training performance.
Your Data Stays Your Data
Whether you use our serverless fine-tuning infra or are running Predibase in your VPC, Predibase ensures your privacy by never retaining your data.
- SaaS
- VPC
Fine-Tune One Base Model For Every Task Type & Serve From A Single Deployment
{"text": .....}
{"text": .....}
{"text": .....}Completions
Leverage continued pre-training to teach your LLM the nuances of domain-specific language with unlabeled datasets.
{"prompt": ...., "completion": .....}
{"prompt": ...., "completion": .....}
{"prompt": ...., "completion": .....}Instruction Tuning
Train your LLMs on specific tasks with structured datasets consisting of (Input, Output) pairs.
{"prompt": ...., "chosen": ....., "rejected": ......}
{"prompt": ...., "chosen": ....., "rejected": ......}
{"prompt": ...., "chosen": ....., "rejected": ......}Direct Preference Optimization (DPO)
Ensure your model’s outputs align with human preferences using a preferences dataset complete with prompts, preferred, and dispreferred responses.
{"messages": [{"role": ..., "content": ...}, {"role": ..., "content": ...}, ...]}
{"messages": [{"role": ..., "content": ...}, {"role": ..., "content": ...}, ...]}
{"messages": [{"role": ..., "content": ...}, {"role": ..., "content": ...}, ...]}Chat
Create chat-specific models for conversational AI by fine-tuning with chat transcripts.
By switching from OpenAI to Predibase we’ve been able to fine-tune and serve many specialized open-source models in real-time, saving us over $1 million annually, while creating engaging experiences for our audiences. Best of all we own the models.
Andres Restrepo, Founder and CEO, Enric.ai
