# 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`] and [`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.