# Save and share a model

Saving a model after quantizing it while loading lets you, and anyone you share it with, skip the quantization step next time. Calibration methods like [GPTQ](./gptq) benefit the most because calibration can take minutes to hours.

## Save the model

Call [save_pretrained()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained) on the quantized model to write the quantized weights and add the `quantization_config` to `config.json`.

```py
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=True)
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-0.6B",
    quantization_config=quantization_config,
    device_map="auto",
)
model.save_pretrained("Qwen3-0.6B-bnb-4bit")
```

Save the tokenizer to the same directory so the checkpoint is complete.

## Push the model to the Hub

Call [push_to_hub()](/docs/transformers/main/en/main_classes/model#transformers.utils.PushToHubMixin.push_to_hub) to upload the quantized weights and config to a Hub repository. Log in with `hf auth login` first.

```py
model.push_to_hub("Qwen3-0.6B-bnb-4bit")
```

## Load the saved model

Load the saved checkpoint with [from_pretrained()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) and no quantization config. Transformers reads the method from the `quantization_config` in `config.json` and loads the weights already quantized.

```py
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("your-username/Qwen3-0.6B-bnb-4bit", device_map="auto")
```

The method's library must be installed wherever the checkpoint is loaded.

## Methods that can't save

Some methods can't save their quantized weights, and [save_pretrained()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained) raises a `ValueError` for them.

| Method | Why |
|---|---|
| [GGUF](./gguf) | Weights load from a GGUF file. Save in GGUF format with [llama.cpp](https://github.com/ggml-org/llama.cpp) tools instead. |
| [NVFP4](./nvfp4) | Saving isn't supported. Quantize the model again when you load it. |
| [Quanto](./quanto) | Saving isn't supported through Transformers. |
| [Quark](./quark) | Saving isn't supported through Transformers. |
| [AWQ](./awq) with an ExLlama backend | Saving isn't supported with the ExLlama backends. Load the model with another backend to save it. |

A quantized model with a [PEFT](../peft) adapter attached is an exception. [save_pretrained()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained) saves only the adapter weights, so it works with any method.

To save a model in full precision instead, dequantize it first. See [Dequantize a model](./dequantize).