# Command Line Interfaces (CLIs) TRL provides a powerful command-line interface (CLI) to fine-tune large language models (LLMs) using methods like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and more. The CLI abstracts away much of the boilerplate, letting you launch training jobs quickly and reproducibly. ## Commands Currently supported commands are: ### Training Commands - `trl dpo`: fine-tune a LLM with DPO - `trl grpo`: fine-tune a LLM with GRPO - `trl kto`: fine-tune a LLM with KTO - `trl reward`: train a Reward Model - `trl rloo`: fine-tune a LLM with RLOO - `trl sft`: fine-tune a LLM with SFT ### Other Commands - `trl env`: get the system information - `trl vllm-serve`: serve a model with vLLM ## Fine-Tuning with the TRL CLI ### Basic Usage You can launch training directly from the CLI by specifying required arguments like the model and dataset: ```bash trl sft \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name stanfordnlp/imdb ``` ```bash trl dpo \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name anthropic/hh-rlhf ``` ```bash trl reward \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name trl-lib/ultrafeedback_binarized ``` ```bash trl grpo \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name HuggingFaceH4/Polaris-Dataset-53K \ --reward_funcs accuracy_reward ``` ```bash trl rloo \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name HuggingFaceH4/Polaris-Dataset-53K \ --reward_funcs accuracy_reward ``` ```bash trl kto \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name trl-lib/kto-mix-14k ``` ### Using Configuration Files To keep your CLI commands clean and reproducible, you can define all training arguments in a YAML configuration file: ```yaml # sft_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: stanfordnlp/imdb ``` Launch with: ```bash trl sft --config sft_config.yaml ``` ```yaml # dpo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: anthropic/hh-rlhf ``` Launch with: ```bash trl dpo --config dpo_config.yaml ``` ```yaml # reward_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: trl-lib/ultrafeedback_binarized ``` Launch with: ```bash trl reward --config reward_config.yaml ``` ```yaml # grpo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: HuggingFaceH4/Polaris-Dataset-53K reward_funcs: - accuracy_reward ``` Launch with: ```bash trl grpo --config grpo_config.yaml ``` ```yaml # rloo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: HuggingFaceH4/Polaris-Dataset-53K reward_funcs: - accuracy_reward ``` Launch with: ```bash trl rloo --config rloo_config.yaml ``` ```yaml # kto_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: trl-lib/kto-mix-14k ``` Launch with: ```bash trl kto --config kto_config.yaml ``` ### Scaling Up with Accelerate TRL CLI natively supports [🤗 Accelerate](https://huggingface.co/docs/accelerate), making it easy to scale training across multiple GPUs, machines, or use advanced setups like DeepSpeed — all from the same CLI. You can pass any `accelerate launch` arguments directly to `trl`, such as `--num_processes`. For more information see [Using accelerate launch](https://huggingface.co/docs/accelerate/en/basic_tutorials/launch#using-accelerate-launch). ```bash trl sft \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name stanfordnlp/imdb \ --num_processes 4 ``` or, with a config file: ```yaml # sft_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: stanfordnlp/imdb num_processes: 4 ``` Launch with: ```bash trl sft --config sft_config.yaml ``` ```bash trl dpo \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name anthropic/hh-rlhf \ --num_processes 4 ``` or, with a config file: ```yaml # dpo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: anthropic/hh-rlhf num_processes: 4 ``` Launch with: ```bash trl dpo --config dpo_config.yaml ``` ```bash trl reward \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name trl-lib/ultrafeedback_binarized \ --num_processes 4 ``` or, with a config file: ```yaml # reward_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: trl-lib/ultrafeedback_binarized num_processes: 4 ``` Launch with: ```bash trl reward --config reward_config.yaml ``` ```bash trl grpo \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name HuggingFaceH4/Polaris-Dataset-53K \ --reward_funcs accuracy_reward \ --num_processes 4 ``` or, with a config file: ```yaml # grpo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: HuggingFaceH4/Polaris-Dataset-53K reward_funcs: - accuracy_reward num_processes: 4 ``` Launch with: ```bash trl grpo --config grpo_config.yaml ``` ```bash trl rloo \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name HuggingFaceH4/Polaris-Dataset-53K \ --reward_funcs accuracy_reward \ --num_processes 4 ``` or, with a config file: ```yaml # rloo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: HuggingFaceH4/Polaris-Dataset-53K reward_funcs: - accuracy_reward num_processes: 4 ``` Launch with: ```bash trl rloo --config rloo_config.yaml ``` ```bash trl kto \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name trl-lib/kto-mix-14k \ --num_processes 4 ``` or, with a config file: ```yaml # kto_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: trl-lib/kto-mix-14k num_processes: 4 ``` Launch with: ```bash trl kto --config kto_config.yaml ``` ### Using `--accelerate_config` for Accelerate Configuration The `--accelerate_config` flag lets you easily configure distributed training with [🤗 Accelerate](https://github.com/huggingface/accelerate). This flag accepts either: - the name of a predefined config profile (built into TRL), or - a path to a custom Accelerate YAML config file. #### Predefined Config Profiles TRL provides several ready-to-use Accelerate configs to simplify common training setups: | Name | Description | | --- | --- | | `fsdp1` | Fully Sharded Data Parallel Stage 1 | | `fsdp2` | Fully Sharded Data Parallel Stage 2 | | `zero1` | DeepSpeed ZeRO Stage 1 | | `zero2` | DeepSpeed ZeRO Stage 2 | | `zero3` | DeepSpeed ZeRO Stage 3 | | `multi_gpu` | Multi-GPU training | | `single_gpu` | Single-GPU training | To use one of these, just pass the name to `--accelerate_config`. TRL will automatically load the corresponding config file from `trl/accelerate_config/`. #### Example Usage ```bash trl sft \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name stanfordnlp/imdb \ --accelerate_config zero2 # or path/to/my/accelerate/config.yaml ``` or, with a config file: ```yaml # sft_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: stanfordnlp/imdb accelerate_config: zero2 # or path/to/my/accelerate/config.yaml ``` Launch with: ```bash trl sft --config sft_config.yaml ``` ```bash trl dpo \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name anthropic/hh-rlhf \ --accelerate_config zero2 # or path/to/my/accelerate/config.yaml ``` or, with a config file: ```yaml # dpo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: anthropic/hh-rlhf accelerate_config: zero2 # or path/to/my/accelerate/config.yaml ``` Launch with: ```bash trl dpo --config dpo_config.yaml ``` ```bash trl reward \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name trl-lib/ultrafeedback_binarized \ --accelerate_config zero2 # or path/to/my/accelerate/config.yaml ``` or, with a config file: ```yaml # reward_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: trl-lib/ultrafeedback_binarized accelerate_config: zero2 # or path/to/my/accelerate/config.yaml ``` Launch with: ```bash trl reward --config reward_config.yaml ``` ```bash trl grpo \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name HuggingFaceH4/Polaris-Dataset-53K \ --reward_funcs accuracy_reward \ --accelerate_config zero2 # or path/to/my/accelerate/config.yaml ``` or, with a config file: ```yaml # grpo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: HuggingFaceH4/Polaris-Dataset-53K reward_funcs: - accuracy_reward accelerate_config: zero2 # or path/to/my/accelerate/config.yaml ``` Launch with: ```bash trl grpo --config grpo_config.yaml ``` ```bash trl rloo \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name HuggingFaceH4/Polaris-Dataset-53K \ --reward_funcs accuracy_reward \ --accelerate_config zero2 # or path/to/my/accelerate/config.yaml ``` or, with a config file: ```yaml # rloo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: HuggingFaceH4/Polaris-Dataset-53K reward_funcs: - accuracy_reward accelerate_config: zero2 # or path/to/my/accelerate/config.yaml ``` Launch with: ```bash trl rloo --config rloo_config.yaml ``` ```bash trl kto \ --model_name_or_path Qwen/Qwen2.5-0.5B \ --dataset_name trl-lib/kto-mix-14k \ --accelerate_config zero2 # or path/to/my/accelerate/config.yaml ``` or, with a config file: ```yaml # kto_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B dataset_name: trl-lib/kto-mix-14k accelerate_config: zero2 # or path/to/my/accelerate/config.yaml ``` Launch with: ```bash trl kto --config kto_config.yaml ``` ### Using dataset mixtures You can use dataset mixtures to combine multiple datasets into a single training dataset. This is useful for training on diverse data sources or when you want to mix different types of data. ```yaml # sft_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B datasets: - path: stanfordnlp/imdb - path: roneneldan/TinyStories ``` Launch with: ```bash trl sft --config sft_config.yaml ``` ```yaml # dpo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B datasets: - path: BAAI/Infinity-Preference - path: argilla/Capybara-Preferences ``` Launch with: ```bash trl dpo --config dpo_config.yaml ``` ```yaml # reward_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B datasets: - path: trl-lib/tldr-preference - path: trl-lib/lm-human-preferences-sentiment ``` Launch with: ```bash trl reward --config reward_config.yaml ``` ```yaml # grpo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B datasets: - path: HuggingFaceH4/Polaris-Dataset-53K - path: trl-lib/DeepMath-103K reward_funcs: - accuracy_reward ``` Launch with: ```bash trl grpo --config grpo_config.yaml ``` ```yaml # rloo_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B datasets: - path: HuggingFaceH4/Polaris-Dataset-53K - path: trl-lib/DeepMath-103K reward_funcs: - accuracy_reward ``` Launch with: ```bash trl rloo --config rloo_config.yaml ``` ```yaml # kto_config.yaml model_name_or_path: Qwen/Qwen2.5-0.5B datasets: - path: trl-lib/kto-mix-14k - path: argilla/ultrafeedback-binarized-preferences-cleaned ``` Launch with: ```bash trl kto --config kto_config.yaml ``` To see all the available keywords for defining dataset mixtures, refer to the [scripts.utils.DatasetConfig](/docs/trl/v1.3.0/en/script_utils#trl.scripts.utils.DatasetConfig) and [DatasetMixtureConfig](/docs/trl/v1.3.0/en/script_utils#trl.DatasetMixtureConfig) classes. ## Getting the System Information You can get the system information by running the following command: ```bash trl env ``` This will print out the system information, including the GPU information, the CUDA version, the PyTorch version, the transformers version, the TRL version, and any optional dependencies that are installed. ```txt Copy-paste the following information when reporting an issue: - Platform: Linux-5.15.0-1048-aws-x86_64-with-glibc2.31 - Python version: 3.11.9 - PyTorch version: 2.4.1 - accelerator(s): NVIDIA H100 80GB HBM3 - Transformers version: 4.45.0.dev0 - Accelerate version: 0.34.2 - Accelerate config: - compute_environment: LOCAL_MACHINE - distributed_type: DEEPSPEED - mixed_precision: no - use_cpu: False - debug: False - num_processes: 4 - machine_rank: 0 - num_machines: 1 - rdzv_backend: static - same_network: True - main_training_function: main - enable_cpu_affinity: False - deepspeed_config: {'gradient_accumulation_steps': 4, 'offload_optimizer_device': 'none', 'offload_param_device': 'none', 'zero3_init_flag': False, 'zero_stage': 2} - downcast_bf16: no - tpu_use_cluster: False - tpu_use_sudo: False - tpu_env: [] - Datasets version: 3.0.0 - HF Hub version: 0.24.7 - TRL version: 0.12.0.dev0+acb4d70 - bitsandbytes version: 0.41.1 - DeepSpeed version: 0.15.1 - Diffusers version: 0.30.3 - Liger-Kernel version: 0.3.0 - LLM-Blender version: 0.0.2 - OpenAI version: 1.46.0 - PEFT version: 0.12.0 - vLLM version: not installed ``` This information is required when reporting an issue.