What it does
Forefront is a platform for building on open-source models. You pick a base model, fine-tune it with your private data, check how it performs on a validation set, and then call it through an API or try it in the Playground. The site shows loss charts during training and a set of built-in evals; MMLU, TruthfulQA, MT-Bench, ARC, HumanEval and AGIEval appear in its examples. Inference runs on serverless endpoints with both chat and completion styles. Models can be exported if you prefer to host them elsewhere, or imported from HuggingFace by pasting a model string. Forefront also stores training, validation and evaluation data: you can pipe production prompts and responses to it in a few lines of code, then inspect samples and token distributions in a data inspector. The site says there are no GPUs or servers to manage, that capacity scales automatically with traffic, and that you pay only for what you use. It also states that requests are not logged and customer data is never used to train models, and that enterprise customers can deploy in a variety of secure clouds.
Capabilities
- Fine-tune open-source models on your own data
- Built-in evals and training loss charts
- Serverless chat and completion API endpoints
- Export models or import them from HuggingFace
- Dataset storage with a data inspector
- Usage-based billing with no charge when idle
When to use it
- You want a model tuned to your own data without running GPU infrastructure
- You want to collect production prompts and responses into datasets ready for later fine-tuning
- You need to compare a fine-tuned model against standard benchmarks before shipping it
- You may want to take the trained model and host it yourself
