# Fine-tune a model

Quantized weights can't be updated by gradients directly, so you fine-tune a quantized model by training a small adapter on top of the frozen quantized weights. Training a [LoRA](https://huggingface.co/docs/peft/main/en/conceptual_guides/lora) adapter on a 4-bit model is known as QLoRA, and it lets you fine-tune a model that wouldn't fit in memory in bf16.

[Trainer](/docs/transformers/main/en/main_classes/trainer#transformers.Trainer) checks the model before training to make sure:

- The quantization method supports training. See the Fine-tune column in [Choose a method](./selecting).
- The model has a trainable adapter attached. Methods that support quantization-aware training are the exception, covered [below](#quantization-aware-training).
- The model isn't compiled with `torch.compile`.

## Train an adapter

Install [PEFT](https://huggingface.co/docs/peft/index).

```bash
pip install -U peft
```

Load the model with a method that supports training, attach a LoRA adapter with [add_adapter()](/docs/transformers/main/en/main_classes/peft#transformers.integrations.PeftAdapterMixin.add_adapter), and pass the model to [Trainer](/docs/transformers/main/en/main_classes/trainer#transformers.Trainer). The example below uses 4-bit [bitsandbytes](./bitsandbytes) and assumes `train_dataset` is a tokenized dataset.

```py
import torch
from peft import LoraConfig
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, Trainer, TrainingArguments

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-0.6B",
    quantization_config=quantization_config,
    device_map="auto",
)

lora_config = LoraConfig(r=16, lora_alpha=32, target_modules="all-linear", task_type="CAUSAL_LM")
model.add_adapter(lora_config)

trainer = Trainer(
    model=model,
    args=TrainingArguments(output_dir="qwen3-qlora", bf16=True),
    train_dataset=train_dataset,
)
trainer.train()
```

Only the adapter weights train. The quantized base weights stay frozen.

## Save the adapter

[save_pretrained()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained) saves only the adapter weights, which are a few megabytes. This works with every method, including ones that can't save their quantized weights.

```py
model.save_pretrained("qwen3-qlora-adapter")
```

To use the adapter, load the base model with the same quantization config and load the adapter on top of it. See [Loading an adapter](../peft#loading-an-adapter) for details.

## Quantization-aware training

[BitNet](./bitnet) and [compressed-tensors](./compressed_tensors) can train the full model without an adapter, as long as the weights stay in a trainable form.

- BitNet needs `linear_class="autobitlinear"` and `quantization_mode="online"` in [BitNetQuantConfig](/docs/transformers/main/en/main_classes/quantization#transformers.BitNetQuantConfig).
- compressed-tensors needs a checkpoint that isn't compressed, or `dequantize=True` in [CompressedTensorsConfig](/docs/transformers/main/en/main_classes/quantization#transformers.CompressedTensorsConfig). Checkpoints that use the FP8 kernels can't train.

See [Quantization concepts](./concept_guide) for how quantization-aware training differs from post-training quantization.