Tools, frameworks, and models for local AI
4 resources found for "Fine-tuning"
The most advanced open image model: excellent prompt adherence, solid anatomy, full fine-tuning. The standard for serious work.
Open source framework for unified fine-tuning of 100+ LLMs and VLMs (Qwen, Llama, Mistral, Gemma): from full SFT to LoRA, QLoRA and DPO, with a built-in WebUI and startup from a single YAML file. The community's reference project (ACL 2024).
Apple's machine learning framework for Apple Silicon: lazy arrays computed across unified CPU and GPU, with unified memory as an asset. The mlx-lm library brings LLM training, fine-tuning (LoRA/QLoRA) and inference directly to the Mac.
Optimized fine-tuning framework for NVIDIA GPUs and Apple Silicon: custom CUDA matrices that cut memory usage and double training speed versus standard methods. Supports LLMs and diffusion models.
