Huggingface Llm Trainer

Train language models on Hugging Face's cloud GPUs without setting up local infrastructure. This handles the full pipeline: SFT, DPO, GRPO, and reward modeling through TRL, plus GGUF conversion for local deployment in Ollama or llama.cpp. The workflow uses UV scripts with PEP 723 inline dependencies, which keeps things clean and self-contained. It includes Trackio monitoring by default and handles the critical parts people usually forget, like making sure your HF_TOKEN gets passed through so trained models actually push to the Hub instead of vanishing when the ephemeral training environment shuts down. Also covers Unsloth integration if you need 60% less VRAM or are training vision-language models where memory gets tight.