Enterprise SLM Platform

Effortless Model Fine-Tuning

Freemium

Description

Enterprise SLM Platform offers an open-source, no-code solution designed for creating domain-specific Small Language Models (SLMs) from custom data sources. It facilitates the fine-tuning of foundational models based on uploaded content, enabling deployment either on-premises or in the cloud. This approach helps businesses and large organizations leverage the power of language models within their private infrastructure, enhancing data security and privacy.

The platform focuses on cost and energy efficiency, aiming to reduce model hallucination and provide accurate information extraction from documents through processing, chunking, and vectorization pipelines. It supports running fine-tuned models directly on devices or behind firewalls, transforming static data into functional AI agents to streamline processes and boost productivity without compromising budget or privacy constraints.

Key Features

No-code Fine-Tuning: Generate datasets and fine-tune models from uploaded documents without coding.
Private Deployment: Designed for private cloud and on-premise use cases.
Fully Managed Service: Offers a platform-as-a-service option.
Cost & Energy Efficient: Provides value and performance with reduced resource consumption.
Edge/Firewall Deployment: Run fine-tuned models on device or behind a firewall.
Private Data Fine-tuning: Utilize private data securely for model specialization.
Document Processing Pipeline: Process, chunk, and vectorize documents for accurate information extraction.
API & Interface: Includes high-level API and user interface.
Efficient Fine-tuning Technique: Primarily uses LoRA (Low Rank Adaption) for efficiency.
Ongoing Support: Offers support, improvements, and updates.

Use Cases

Creating domain-specific language models for specialized applications.
Developing custom AI agents from static company data.
Streamlining internal processes with AI tailored to specific business knowledge.
Deploying language models in secure, private environments (on-premise/private cloud).
Reducing LLM operational costs and energy usage for organizations.

Frequently Asked Questions

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.

What 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.

How 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.

What 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. If you want to increase this limit, please reach out to us: hello@smartloop.ai