🏭 TRL Jobs

TRL Jobs is a simple wrapper around Hugging Face Jobs that makes it easy to run TRL (Transformer Reinforcement Learning) workflows directly on 🤗 Hugging Face infrastructure.

Think of it as the quickest way to kick off Supervised Fine-Tuning (SFT) and more, without worrying about all the boilerplate setup.

📦 Installation

Get started with a single command:

pip install trl-jobs

⚡ Quick Start

Run your first supervised fine-tuning job in just one line:

trl-jobs sft --model_name Qwen/Qwen3-0.6B --dataset_name trl-lib/Capybara

The training is tracked with Trackio and the fine-tuned model is automatically pushed to the 🤗 Hub.

🛠 Available Commands

Right now, SFT (Supervised Fine-Tuning) is supported. More workflows will be added soon!

🔹 SFT (Supervised Fine-Tuning)

trl-jobs sft --model_name Qwen/Qwen3-0.6B --dataset_name trl-lib/Capybara

Required arguments

• --model_name → Model to fine-tune (e.g. Qwen/Qwen3-0.6B)
• --dataset_name → Dataset to train on (e.g. trl-lib/Capybara)

Optional arguments

• --peft → Use PEFT (LoRA) (default: False)
• --flavor → Hardware flavor (default: a100-large, only option for now)
• --timeout → Max runtime (1h by default). Supports s, m, h, d
• -d, --detach → Run in background and print job ID
• --namespace → Namespace where the job will run (default: your user namespace)
• --token → Hugging Face token (only needed if not logged in)

➡️ You can also pass any arguments supported by trl sft. E.g.

trl-jobs sft --model_name Qwen/Qwen3-0.6B --dataset_name trl-lib/Capybara --learning_rate 3e-5

For the full list, see the TRL CLI docs.

Dataset format

SFT supports various 4 dataset formats.

• Standard language modeling

  example = {"text": "The sky is blue."}
  

• Standard prompt-completion

  example = {"prompt": "The sky is", "completion": " blue."}
  

• Conversationanl language modeling

  example = {"messages": [
      {"role": "user", "content": "What color is the sky?"},
      {"role": "assistant", "content": "It is blue."}
  ]}
  

• Conversational prompt-completion

  example = {"prompt": [{"role": "user", "content": "What color is the sky?"}],
             "completion": [{"role": "assistant", "content": "It is blue."}]}
  

[!IMPORTANT] When using conversational dataset, ensure that the model has a chat template.

[!NOTE] When using prompt-completion dataset, the loss is only computed on the completion part.

For more details, see the TRL docs - Dataset formats.

📊 Supported Configurations

Here are some ready-to-go setups you can use out of the box.

🦙 Meta LLaMA 3

🦙 Meta LLaMA 3 with PEFT

🐧 Qwen3

🐧 Qwen3 with PEFT

SmolLM3

🤖 OpenAI GPT-OSS (with PEFT)

🚧 Coming soon!

💡 Want support for another model?

Open an issue or submit a PR—we’d love to hear from you!

🔑 Authentication

You’ll need a Hugging Face token to run jobs. You can provide it in any of these ways:

1. Login with huggingface-cli login
2. Set the environment variable HF_TOKEN
3. Pass it directly with --token

📜 License

This project is under the MIT License. See the LICENSE file for details.

🤝 Contributing

We welcome contributions! Please open an issue or a PR on GitHub.

Before committing, run formatting checks:

ruff check . --fix && ruff format . --line-length 119