model-trainer

To fine-tune a language model with TRL on Hugging Face Jobs, submit an inline UV training script with appropriate hardware flavor and timeout settings. The workflow supports SFT, DPO, GRPO, and reward-modeling workflows, validating datasets and pushing artifacts to the Hub using HF_TOKEN.

The best way to monitor TRL training metrics on Hugging Face Jobs is by integrating Trackio for real-time metrics. This production-tested workflow includes Trackio monitoring directly within the training script, allowing you to observe fine-tuning progress and catch issues during SFT or DPO workflows.

Yes, you can convert fine-tuned Hugging Face adapters to GGUF format for local deployment. This workflow handles the conversion of trained models to GGUF, enabling local inference with frameworks like Ollama, llama.cpp, or LM Studio after your TRL training job completes.

TRL training on Hugging Face Jobs requires explicit timeout settings and appropriate hardware flavor selection based on your model size and training workload. You must configure these parameters inline when submitting your UV job, alongside including HF_TOKEN in secrets for Hub pushes.

Hugging Face Jobs training workflows fail to push fine-tuned models when HF_TOKEN is not included in secrets. This workflow enforces hub-push best practices by requiring HF_TOKEN in secrets for authentication, alongside dataset validation and recovery guidelines to prevent lost work.

Yes, TRL supports GRPO and reward-modeling workflows on managed Hugging Face Jobs. You can submit these workflows using inline UV scripts or official TRL examples, applying SFT, DPO, GRPO, and reward-modeling techniques with dataset validation and Trackio monitoring integrated.