Build LLM projects with DagsHub

Efficiently log prompts and experiments, ensuring transparency and mitigating biases or hallucinations in your LLM. Track and evaluate data for accurate model assessment, while labeling data for RLHF.

Your source of truth for LLM customization

RLHF Annotations

Rank and categorize your LLM generations so that you can perform reinforcement learning from human feedback

Integrated data annotation
Visual ranking templates

LLM Evaluation

Curate datasets of prompts and generations to better evaluate your model.
Reduce the chances of your model being biased/hallucinating with unlikely results

Data curation
Datasets versioning

Prompt Tracking

Track your prompts and LLM responses, visualize and compare results of exprimental submissions

Prompt logging
Manage customized LLM models

Zero DevOps!

Avoid the MLOps “tedious work” by using DagsHub’s capabilities without relying on your DevOps.

We do the heavy lifting for you

We will host the servers for data versioning, labeling, and experiment tracking and set up a central repository, so you can just work on ML

No need to be familiar with different tools

You will use a bunch of great tools, but work with only one interface, much more convenient

Get started working faster

Since we’re doing all the setup,
your step 1 is machine learning work!

Don’t just take our word for it..

Transform your ML development with DagsHub – try it now

Fresh, from our blog

Google Colab

DagsHub Integrates with Colab: Build And Train ML Models With ZERO MLOps

Nir Barazida

View post

Storage

Connect S3-Compatible Storage To DagsHub: Manage Data And Code In The Same Place

Kang-Chi Ho

View post

MLOps

How to Setup Kubeflow on AWS Using Terraform

Yono Mittlefehldt

View post

DVC

Getting Started With DVC

Eugenia Anello

View post