ChatLLaMA or Predibase? A Comprehensive Review

Runs directly on GPUs

Utilizes trained LoRA

Models conversational systems

Future RLHF version

Available in multiple models

Accepts user-shared datasets

Trainable on new datasets

Includes Desktop GUI

Operates locally

Designed for research

Low-code platform

Fast ML model training

Efficient deployment

Minimal configuration code needed

Large language models support

Audio classification support

Bot detection capability

Fraud detection feature

Suitable for customer sentiment analysis

Topic classification functionality

Requires JavaScript to buy

Runs directly on GPUs

No foundation model weights

Designed primarily for research

Dependent on user data share

Limited to 30B

13B

and 7B models

Communication mainly through Discord

Additional RLHF version not available

Complex configuration code required

Limited to certain ML models

Built on specific open-source technologies

Requires granular-level model adjustments

Private model hosting not default

Deployment requires specific infrastructure knowledge

Excessively developer-focused

less for non-tech

Requires historical data for use

Proven scalability not explicitly stated