Effortless
Model
Fine-Tuning
An open-source and no-code Small Language Model platform to create domain specificmodel from custom , saving cost and energy while reducing hallucination.
Platform features
Designed for private cloud and on-prem use cases tailored towards businesses and large organizations
No-code dataset generation and fine-tuning a model based on an uploaded document.
Fully managed platform as a service
Energy and cost-efficient solution giving you the best value and performance
Run the fine-tuned model on device or behind a firewall
What we offer
Fine-tuning of models using private data
Pipeline to process, chunk and vectorize documents to extract information accurately
Cost-effective solution tailored to your needs
High-level API and Interface
Installation on edge or private cloud
Support and on-going improvements and updates
FAQ
01 What is fine-tuning a model?
01 What is fine-tuning a model?
The purpose of fine-tuning is to convert a model into a more specialized version for a given dataset. This enhances the model's accuracy for a specific topic or domain.
02What is baseline models vs fine-tuned models?
02What is baseline models vs fine-tuned models?
Baseline models like GPT-4 are well-suited for general-purpose reasoning, whereas fine-tuned models are primarily used to create domain-specific LLMs for more specialized applications.
03How do you fine-tune models?
03How do you fine-tune models?
We use different techniques but primarily use LoRA (Low Rank Adaption) to fine-tune models which makes it efficient in terms of memory, loading and un-loading of models.
04What is a token limit?
04What is a token limit?
Token limits are restrictions on the number of tokens that an LLM can process in a single interaction. In the context of the project, it is the number of tokens supported per project. For a free plan, it is 16K. This good enough for a small sized and personal projects. If you want to increase this limit, please reach out to us: hello@smartloop.ai
Use the power of Large Language Model in a small footprint, covering your static data into Agents to streamline your existing process and be more productive without compromising your privacy within your budget