FinetuneDB

About FinetuneDB

FinetuneDB provides an end-to-end platform for fine-tuning large language models (LLMs) by enabling users to create, manage, and optimize proprietary datasets quickly and collaboratively. This solution reduces the time and cost associated with model training while enhancing performance through automated evaluations and production data tracking.

```xml <problem> Fine-tuning large language models (LLMs) requires significant time and resources for creating, managing, and optimizing proprietary datasets. Traditional methods often lack collaborative features and efficient evaluation tools, leading to increased costs and slower model improvement cycles. </problem> <solution> FinetuneDB offers an end-to-end platform designed to streamline the LLM fine-tuning process, enabling users to build custom AI models with their data more efficiently. The platform provides a collaborative environment for teams to create and manage fine-tuning datasets, track model performance, and optimize prompts. By automating evaluations, capturing production data, and offering tools for prompt engineering, FinetuneDB aims to reduce the time and cost associated with model training while enhancing the performance of custom LLMs. The platform integrates with the OpenAI SDK and offers SDKs for Python and JavaScript/TypeScript, facilitating easy integration into existing production applications. </solution> <features> - Collaborative dataset editor for team-based creation and management of fine-tuning datasets. - Automated evaluations and model improvements using the Copilot feature. - Tools for benchmarking outputs and tracking AI metrics such as speed, quality scores, and token usage. - Production data logging to capture user interactions, model responses, and system metrics for ongoing model improvement. - Advanced filters for refining searches and identifying relevant data for fine-tuning. - Prompt playground for creating, managing, and optimizing prompts. - Version control for tracking changes to prompts and models. - Integrations with OpenAI SDK, Langchain, and SDKs for Python and JavaScript/TypeScript. - Secure encryption of data in transit and at rest using industry-standard AES 256 encryption. - Strict permissions enforcement for managing user roles and permissions. </features> <target_audience> FinetuneDB targets AI developers, product managers, and domain experts who are building and fine-tuning LLMs for specific use cases and require a collaborative, efficient, and secure platform. </target_audience> ```

What does FinetuneDB do?

FinetuneDB provides an end-to-end platform for fine-tuning large language models (LLMs) by enabling users to create, manage, and optimize proprietary datasets quickly and collaboratively. This solution reduces the time and cost associated with model training while enhancing performance through automated evaluations and production data tracking.

Where is FinetuneDB located?

FinetuneDB is based in Stockholm, Sweden.

When was FinetuneDB founded?

FinetuneDB was founded in 2023.

How much funding has FinetuneDB raised?

FinetuneDB has raised 163389.

Who founded FinetuneDB?

FinetuneDB was founded by Felix Wunderlich and Farouq Aldori.

  • Felix Wunderlich - Co-Founder
  • Farouq Aldori - Co-founder/CTO
Location
Stockholm, Sweden
Founded
2023
Funding
163389
Employees
3 employees
Major Investors
Antler
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FinetuneDB

Score: 88/100
AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

FinetuneDB provides an end-to-end platform for fine-tuning large language models (LLMs) by enabling users to create, manage, and optimize proprietary datasets quickly and collaboratively. This solution reduces the time and cost associated with model training while enhancing performance through automated evaluations and production data tracking.

finetunedb.com200+
cb
Crunchbase
Founded 2023Stockholm, Sweden

Funding

$

Estimated Funding

$163.4K+

Major Investors

Antler

Team (<5)

Felix Wunderlich

Co-Founder

Farouq Aldori

Co-founder/CTO

Company Description

Problem

Fine-tuning large language models (LLMs) requires significant time and resources for creating, managing, and optimizing proprietary datasets. Traditional methods often lack collaborative features and efficient evaluation tools, leading to increased costs and slower model improvement cycles.

Solution

FinetuneDB offers an end-to-end platform designed to streamline the LLM fine-tuning process, enabling users to build custom AI models with their data more efficiently. The platform provides a collaborative environment for teams to create and manage fine-tuning datasets, track model performance, and optimize prompts. By automating evaluations, capturing production data, and offering tools for prompt engineering, FinetuneDB aims to reduce the time and cost associated with model training while enhancing the performance of custom LLMs. The platform integrates with the OpenAI SDK and offers SDKs for Python and JavaScript/TypeScript, facilitating easy integration into existing production applications.

Features

Collaborative dataset editor for team-based creation and management of fine-tuning datasets.

Automated evaluations and model improvements using the Copilot feature.

Tools for benchmarking outputs and tracking AI metrics such as speed, quality scores, and token usage.

Production data logging to capture user interactions, model responses, and system metrics for ongoing model improvement.

Advanced filters for refining searches and identifying relevant data for fine-tuning.

Prompt playground for creating, managing, and optimizing prompts.

Version control for tracking changes to prompts and models.

Integrations with OpenAI SDK, Langchain, and SDKs for Python and JavaScript/TypeScript.

Secure encryption of data in transit and at rest using industry-standard AES 256 encryption.

Strict permissions enforcement for managing user roles and permissions.

Target Audience

FinetuneDB targets AI developers, product managers, and domain experts who are building and fine-tuning LLMs for specific use cases and require a collaborative, efficient, and secure platform.