Train AI modelslocally & fine-tune them.

Yes. FrameTrain supports fine-tuning of Large Language Models (LLMs) via LoRA and QLoRA. You can import models like Llama, Mistral, or Phi directly from HuggingFace and adapt them to your own datasets.

FrameTrain runs on all NVIDIA GPUs with CUDA support (from the GTX 10 series onward) as well as on Apple Silicon (M1/M2/M3/M4) via Metal Performance Shaders. Pure CPU-based training is also possible.

FrameTrain runs entirely locally on your machine. There are no ongoing cloud costs, your data is never uploaded, and you're not dependent on an internet connection. Ideal for sensitive or proprietary datasets.

No. FrameTrain is designed as a graphical application. You configure training parameters through an intuitive interface without writing any code. Advanced users can also use configuration files.

Yes, LoRA (Low-Rank Adaptation) is fully integrated. You can configure LoRA rank, alpha, and target modules directly in the app, which also enables fine-tuning on GPUs with limited VRAM.

FrameTrain is in Early Access. The price is intentionally low so that as many developers and researchers as possible can get access early. The price increases to €9.99 after the first 100 users – sign up now to lock in the lower price permanently.

Yes. FrameTrain natively supports Apple Silicon via Metal Performance Shaders (MPS). Models are accelerated on the M-chip's GPU without requiring CUDA.

FrameTrain supports PyTorch models (.pt, .bin), SafeTensors, and the GGUF format. Models can be imported directly from the HuggingFace Hub or loaded locally.

Yes. You can load your own datasets in CSV, JSON, or JSONL format. FrameTrain also supports datasets directly from the HuggingFace Datasets Hub.

Yes. FP16 and BF16 mixed-precision training are fully supported, as is gradient checkpointing for memory-efficient training on GPUs with limited VRAM.