Buckets:
| # TRL Integration | |
| Trackio integrates natively with [TRL](https://github.com/huggingface/trl) so you can log metrics from any TRL trainer (`SFTTrainer`, `DPOTrainer`, `GRPOTrainer`, etc.) with minimal setup. Ensure you have the latest version of `trl` installed (version 1.2.0 or higher). | |
| ```python | |
| from datasets import Dataset | |
| from trl import SFTConfig, SFTTrainer | |
| # Create a small fake dataset | |
| prompts = ["The capital of France is", "Hamlet was written by"] * 12 | |
| completions = [" Paris.", " Shakespeare."] * 12 | |
| dataset = Dataset.from_dict({"prompt": prompts, "completion": completions}) | |
| # Train a model using the TRL SFTTrainer API | |
| trainer = SFTTrainer( | |
| model="Qwen/Qwen3-0.6B", | |
| args=SFTConfig(report_to="trackio", run_name="Qwen3-0.6B-sft"), | |
| train_dataset=dataset, | |
| ) | |
| trainer.train() | |
| ``` | |
| ## Configuring Project and Space | |
| Set the project and space ID directly in your TRL config (e.g. [SFTConfig](https://huggingface.co/docs/trl/main/en/sft_trainer#trl.SFTConfig), [DPOConfig](https://huggingface.co/docs/trl/main/en/dpo_trainer#trl.DPOConfig), [GRPOConfig](https://huggingface.co/docs/trl/main/en/grpo_trainer#trl.GRPOConfig)): | |
| ```python | |
| from trl import SFTConfig | |
| args = SFTConfig( | |
| report_to="trackio", | |
| run_name="my-run", | |
| project="my-project", | |
| trackio_space_id="username/space_id", | |
| ) | |
| ``` | |
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