SFT Trainer

Overview

TRL supports the Supervised Fine-Tuning (SFT) Trainer for training language models.

This post-training method was contributed by Younes Belkada.

Quick start

This example demonstrates how to train a language model using the [SFTTrainer] from TRL. We train a Qwen 3 0.6B model on the Capybara dataset, a compact, diverse multi-turn dataset to benchmark reasoning and generalization.

from trl import SFTTrainer, SFTConfig
from datasets import load_dataset

trainer = SFTTrainer(
    model="Qwen/Qwen3-0.6B",
    train_dataset=load_dataset("trl-lib/Capybara", split="train"),
)
trainer.train()

Expected dataset type and format

SFT supports both language modeling and prompt-completion datasets. The [SFTTrainer] is compatible with both standard and conversational dataset formats. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.

# Standard language modeling
{"text": "The sky is blue."}

# Conversational language modeling
{"messages": [{"role": "user", "content": "What color is the sky?"},
              {"role": "assistant", "content": "It is blue."}]}

# Standard prompt-completion
{"prompt": "The sky is",
 "completion": " blue."}

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

If your dataset is not in one of these formats, you can preprocess it to convert it into the expected format. Here is an example with the FreedomIntelligence/medical-o1-reasoning-SFT dataset:

from datasets import load_dataset

dataset = load_dataset("FreedomIntelligence/medical-o1-reasoning-SFT", "en")

def preprocess_function(example):
    return {
        "prompt": [{"role": "user", "content": example["Question"]}],
        "completion": [
            {"role": "assistant", "content": f"<think>{example['Complex_CoT']}</think>{example['Response']}"}
        ],
    }

dataset = dataset.map(preprocess_function, remove_columns=["Question", "Response", "Complex_CoT"])
print(next(iter(dataset["train"])))

{
    "prompt": [
        {
            "content": "Given the symptoms of sudden weakness in the left arm and leg, recent long-distance travel, and the presence of swollen and tender right lower leg, what specific cardiac abnormality is most likely to be found upon further evaluation that could explain these findings?",
            "role": "user",
        }
    ],
    "completion": [
        {
            "content": "<think>Okay, let's see what's going on here. We've got sudden weakness [...] clicks into place!</think>The specific cardiac abnormality most likely to be found in [...] the presence of a PFO facilitating a paradoxical embolism.",
            "role": "assistant",
        }
    ],
}

Looking deeper into the SFT method

Supervised Fine-Tuning (SFT) is the simplest and most commonly used method to adapt a language model to a target dataset. The model is trained in a fully supervised fashion using pairs of input and output sequences. The goal is to minimize the negative log-likelihood (NLL) of the target sequence, conditioning on the input.

This section breaks down how SFT works in practice, covering the key steps: preprocessing, tokenization and loss computation.

Preprocessing and tokenization

During training, each example is expected to contain a text field or a (prompt, completion) pair, depending on the dataset format. For more details on the expected formats, see Dataset formats. The SFTTrainer tokenizes each input using the model's tokenizer. If both prompt and completion are provided separately, they are concatenated before tokenization.

Computing the loss

The loss used in SFT is the token-level cross-entropy loss, defined as:

$\mathcal{L}_{\text{SFT}}(\theta) = - \sum_{t=1}^{T} \log p_\theta(y_t \mid y_{<t}),$

where $y_{t}$ is the target token at timestep $t$ , and the model is trained to predict the next token given the previous ones. In practice, padding tokens are masked out during loss computation.

Label shifting and masking

During training, the loss is computed using a one-token shift: the model is trained to predict each token in the sequence based on all previous tokens. Specifically, the input sequence is shifted right by one position to form the target labels. Padding tokens (if present) are ignored in the loss computation by applying an ignore index (default: -100) to the corresponding positions. This ensures that the loss focuses only on meaningful, non-padding tokens.

Logged metrics

global_step: The total number of optimizer steps taken so far.
epoch: The current epoch number, based on dataset iteration.
num_tokens: The total number of tokens processed so far.
loss: The average cross-entropy loss computed over non-masked tokens in the current logging interval.
mean_token_accuracy: The proportion of non-masked tokens for which the model’s top-1 prediction matches the ground truth token.
learning_rate: The current learning rate, which may change dynamically if a scheduler is used.
grad_norm: The L2 norm of the gradients, computed before gradient clipping.

Customization

Model initialization

You can directly pass the kwargs of the [~transformers.AutoModelForCausalLM.from_pretrained()] method to the [SFTConfig]. For example, if you want to load a model in a different precision, analogous to

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", torch_dtype=torch.bfloat16)

you can do so by passing the model_init_kwargs={"torch_dtype": torch.bfloat16} argument to the [SFTConfig].

from trl import SFTConfig

training_args = SFTConfig(
    model_init_kwargs={"torch_dtype": torch.bfloat16},
)

Note that all keyword arguments of [~transformers.AutoModelForCausalLM.from_pretrained()] are supported.

Packing

[SFTTrainer] supports example packing, where multiple examples are packed in the same input sequence to increase training efficiency. To enable packing, simply pass packing=True to the [SFTConfig] constructor.

training_args = SFTConfig(packing=True)

For more details on packing, see Packing.

Train on assistant messages only

To train on assistant messages only, use a conversational dataset and set assistant_only_loss=True in the [SFTConfig]. This setting ensures that loss is computed only on the assistant responses, ignoring user or system messages.

training_args = SFTConfig(assistant_only_loss=True)

This functionality is only available for chat templates that support returning the assistant tokens mask via the {% generation %} and {% endgeneration %} keywords. For an example of such a template, see HugggingFaceTB/SmolLM3-3B.

Train on completion only

To train on completion only, use a prompt-completion dataset. By default, the trainer computes the loss on the completion tokens only, ignoring the prompt tokens. If you want to train on the full sequence, set completion_only_loss=False in the [SFTConfig].

Training on completion only is compatible with training on assistant messages only. In this case, use a [conversational](dataset_formats#conversational) [prompt-completion](dataset_formats#prompt-completion) dataset and set `assistant_only_loss=True` in the [`SFTConfig`].

Train adapters with PEFT

We support tight integration with 🤗 PEFT library, allowing any user to conveniently train adapters and share them on the Hub, rather than training the entire model.

from datasets import load_dataset
from trl import SFTTrainer
from peft import LoraConfig

dataset = load_dataset("trl-lib/Capybara", split="train")

trainer = SFTTrainer(
    "Qwen/Qwen3-0.6B",
    train_dataset=dataset,
    peft_config=LoraConfig()
)

trainer.train()

You can also continue training your [peft.PeftModel]. For that, first load a PeftModel outside [SFTTrainer] and pass it directly to the trainer without the peft_config argument being passed.

from datasets import load_dataset
from trl import SFTTrainer
from peft import AutoPeftModelForCausalLM

model = AutoPeftModelForCausalLM.from_pretrained("trl-lib/Qwen3-4B-LoRA", is_trainable=True)
dataset = load_dataset("trl-lib/Capybara", split="train")

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
)

trainer.train()

When training adapters, you typically use a higher learning rate (≈1e‑4) since only new parameters are being learned.

SFTConfig(learning_rate=1e-4, ...)

Train with Liger Kernel

Liger Kernel is a collection of Triton kernels for LLM training that boosts multi-GPU throughput by 20%, cuts memory use by 60% (enabling up to 4× longer context), and works seamlessly with tools like FlashAttention, PyTorch FSDP, and DeepSpeed. For more information, see Liger Kernel Integration.

Train with Unsloth

Unsloth is an open‑source framework for fine‑tuning and reinforcement learning that trains LLMs (like Llama, Mistral, Gemma, DeepSeek, and more) up to 2× faster with up to 70% less VRAM, while providing a streamlined, Hugging Face–compatible workflow for training, evaluation, and deployment. For more information, see Unsloth Integration.

Instruction tuning example

Instruction tuning teaches a base language model to follow user instructions and engage in conversations. This requires:

Chat template: Defines how to structure conversations into text sequences, including role markers (user/assistant), special tokens, and turn boundaries. Read more about chat templates in Chat templates.
Conversational dataset: Contains instruction-response pairs

This example shows how to transform the Qwen 3 0.6B Base model into an instruction-following model using the Capybara dataset and a chat template from HuggingFaceTB/SmolLM3-3B. The SFT Trainer automatically handles tokenizer updates and special token configuration.

from trl import SFTTrainer, SFTConfig
from datasets import load_dataset

trainer = SFTTrainer(
    model="Qwen/Qwen3-0.6B-Base",
    args=SFTConfig(
        output_dir="Qwen3-0.6B-Instruct",
        chat_template_path="HuggingFaceTB/SmolLM3-3B",
    ),
    train_dataset=load_dataset("trl-lib/Capybara", split="train"),
)
trainer.train()

Some base models, like those from Qwen, have a predefined chat template in the model's tokenizer. In these cases, it is not necessary to apply [clone_chat_template()], as the tokenizer already handles the formatting. However, it is necessary to align the EOS token with the chat template to ensure the model's responses terminate correctly. In these cases, specify eos_token in [SFTConfig]; for example, for Qwen/Qwen2.5-1.5B, one should set eos_token="<|im_end|>".

Once trained, your model can now follow instructions and engage in conversations using its new chat template.

>>> from transformers import pipeline
>>> pipe = pipeline("text-generation", model="Qwen3-0.6B-Instruct/checkpoint-5000")
>>> prompt = "<|im_start|>user\nWhat is the capital of France? Answer in one word.<|im_end|>\n<|im_start|>assistant\n"
>>> response = pipe(prompt)
>>> response[0]["generated_text"]
'<|im_start|>user\nWhat is the capital of France? Answer in one word.<|im_end|>\n<|im_start|>assistant\nThe capital of France is Paris.'

Alternatively, use the structured conversation format (recommended):

>>> prompt = [{"role": "user", "content": "What is the capital of France? Answer in one word."}]
>>> response = pipe(prompt)
>>> response[0]["generated_text"]
[{'role': 'user', 'content': 'What is the capital of France? Answer in one word.'}, {'role': 'assistant', 'content': 'The capital of France is Paris.'}]

Tool Calling with SFT

The SFT trainer fully supports fine-tuning models with tool calling capabilities. In this case, each dataset example should include:

The conversation messages, including any tool calls (tool_calls) and tool responses (tool role messages)
The list of available tools in the tools column, typically provided as JSON schemas

For details on the expected dataset structure, see the Dataset Format — Tool Calling section.

Extending `SFTTrainer` for Vision Language Models

SFTTrainer does not yet inherently support vision-language data. However, we provide a guide on how to tweak the trainer to support vision-language data. Specifically, you need to use a custom data collator that is compatible with vision-language data. This guide outlines the steps to make these adjustments. For a concrete example, refer to the script examples/scripts/sft_vlm.py, which demonstrates how to fine-tune the LLaVA 1.5 model on the HuggingFaceH4/llava-instruct-mix-vsft dataset.

Preparing the Data

The data format is flexible, provided it is compatible with the custom collator that we will define later. A common approach is to use conversational data. Given that the data includes both text and images, the format needs to be adjusted accordingly. Below is an example of a conversational data format involving both text and images:

images = ["obama.png"]
messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Who is this?"},
            {"type": "image"}
        ]
    },
    {
        "role": "assistant",
        "content": [
            {"type": "text", "text": "Barack Obama"}
        ]
    },
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "What is he famous for?"}
        ]
    },
    {
        "role": "assistant",
        "content": [
            {"type": "text", "text": "He is the 44th President of the United States."}
        ]
    }
]

To illustrate how this data format will be processed using the LLaVA model, you can use the following code:

from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
print(processor.apply_chat_template(messages, tokenize=False))

The output will be formatted as follows:

Who is this? ASSISTANT: Barack Obama USER: What is he famous for? ASSISTANT: He is the 44th President of the United States.

A custom collator for processing multi-modal data

Unlike the default behavior of [SFTTrainer], processing multi-modal data is done on the fly during the data collation process. To do this, you need to define a custom collator that processes both the text and images. This collator must take a list of examples as input (see the previous section for an example of the data format) and return a batch of processed data. Below is an example of such a collator:

def collate_fn(examples):
    # Get the texts and images, and apply the chat template
    texts = [processor.apply_chat_template(example["messages"], tokenize=False) for example in examples]
    images = [example["images"][0] for example in examples]

    # Tokenize the texts and process the images
    batch = processor(images=images, text=texts, return_tensors="pt", padding=True)

    # The labels are the input_ids, and we mask the padding tokens in the loss computation
    labels = batch["input_ids"].clone()
    labels[labels == processor.tokenizer.pad_token_id] = -100
    batch["labels"] = labels

    return batch

We can verify that the collator works as expected by running the following code:

from datasets import load_dataset

dataset = load_dataset("HuggingFaceH4/llava-instruct-mix-vsft", split="train")
examples = [dataset[0], dataset[1]]  # Just two examples for the sake of the example
collated_data = collate_fn(examples)
print(collated_data.keys())  # dict_keys(['input_ids', 'attention_mask', 'pixel_values', 'labels'])

Training the vision-language model

Now that we have prepared the data and defined the collator, we can proceed with training the model. To ensure that the data is not processed as text-only, we need to set a couple of arguments in the [SFTConfig], specifically remove_unused_columns and skip_prepare_dataset to True to avoid the default processing of the dataset. Below is an example of how to set up the SFTTrainer.

training_args.remove_unused_columns = False
training_args.dataset_kwargs = {"skip_prepare_dataset": True}

trainer = SFTTrainer(
    model=model,
    args=training_args,
    data_collator=collate_fn,
    train_dataset=train_dataset,
    processing_class=processor,
)

A full example of training LLaVa 1.5 on the HuggingFaceH4/llava-instruct-mix-vsft dataset can be found in the script examples/scripts/sft_vlm.py.

SFTTrainer

[[autodoc]] SFTTrainer - train - save_model - push_to_hub

SFTConfig

[[autodoc]] SFTConfig