hugging-face-model-trainer

You can train a language model on Hugging Face Jobs using TRL by submitting an inline UV/TRL script configured with SFT, DPO, GRPO, or Reward Modeling methods. The workflow handles dataset preparation, hardware selection, and cost estimation automatically.

Yes, Hugging Face Jobs supports SFT, DPO, GRPO, and Reward Modeling training methods through TRL. You can configure these methods using inline UV/TRL scripts to execute your training workflow on cloud GPUs.

Yes, you can convert your trained model to GGUF format for local deployment compatibility. The training workflow on Hugging Face Jobs includes GGUF conversion support alongside model persistence to the Hub.

Yes, you need an HF_TOKEN for hub authentication when running TRL training jobs. The workflow requires HF_TOKEN-based secrets along with hub_model_id and push_to_hub configuration to persist your trained model to the Hub.

You can monitor cloud GPU training runs on Hugging Face Jobs using Trackio integration. The workflow incorporates Trackio monitoring to track training progress, requiring appropriate timeout settings for long-running training jobs.

The best way to configure push_to_hub for TRL model training is to set the hub_model_id and push_to_hub configuration parameters in your inline UV/TRL script. This ensures your trained model is automatically persisted to the Hugging Face Hub.