Major Update: Streamline your model fine-tuning process with our new No-Code SDXL FineTuner. Fine-tuning your model usually requires adjusting a multitude of parameters, but we've simplified it down to just three straightforward steps!

We've added a dedicated page for Application Projects to the UI. This page showcases the range of projects achievable with Monster API models. Users can explore these projects and access Colab notebooks for streamlined deployment.

Documentation now includes SDK docs for Python and Node.js clients. These clients facilitate easier access to our models.

The Photo Maker Model has been introduced to our suite of models. This tool can create photos, paintings, and avatars in various styles and is now available on MonsterAPI as an ultra-low-cost API.

The user experience has been enhanced by enabling the upload of up to 20 images. This upgrade streamlines the process of uploading images for SDXL fine-tuning directly from the UI, leading to easier dataset control.

A redesigned Billing Page UI has been implemented for improved usability and functionality. Features like Auto Recharge, Change Plan, and the Plan Calculator have been integrated to streamline account management processes.

MonsterAPI now supports Mixtral 8x7B model deployment, enabling hosting Mixtral as an API endpoint on the GPU cloud. Query and fetch responses on-demand with Monster Deploy, the optimized LLM deployment engine designed for higher throughput and lower cost with batching.

Transitioned from overage billing to auto-recharge method for improved service continuity. When the credit balance falls below 5,000, it automatically recharges to maintain a balance above 0 credits, simplifying credit monitoring.

Two new models have been added to our no-code LLM finetuner:

A temporary file size limit of 275 MB is being implemented for our speech-to-text APIs. This limit is sufficient to accommodate audio files of up to 3 hours in length. Users are advised to adjust their API calls accordingly. This adjustment aims to enhance the stability of our speech-to-text transcription systems, resulting in an almost 100% success rate.