# Incompatible changes between v1.2 and v1.1

## User-facing APIs

The user-facing APIs are changed between v1.2 and v1.1. The major changes are:

1. v1.2 abstracts `neural_compressor.common.Model` concept to cover those cases whose weight and graph files are stored separately.

2. v1.2 unifies the calling style by setting model, calibration dataloader, evaluation dataloader, and metric through `quantizer` attributes rather than passing as function inputs.

Refer to below examples for details.

```python
# user facing API example in v1.1
quantizer = Quantization("/path/to/user.yaml")
ds = dataset("/path/to/dataset")
dataloader = quantizer.dataloader(ds, batch_size=100)
quantizer.metric("metric", metric)
q_model = quantizer(
    "/path/to/model",
    q_dataloader=dataloader,
    eval_dataloader=dataloader,
)
...  # user to write framework specific code to save q_model
```

```python
# user facing API example in v1.2
quantizer = Quantization(conf.yaml)
quantizer.model = "/path/to/model"
dl = dataset("/path/to/dataset")
quantizer.calib_dataloader = common.DataLoader(dl, batch_size=32)
quantizer.eval_dataloader = common.DataLoader(dl, batch_size=32)
quantizer.metric = common.Metric(custom_metric)
q_model = quantizer.fit()
q_model.save("/path/to/output/dir")  # explicitly call to save q_model
```

## Built-in transform/dataset/metric APIs

v1.2 refines Neural Compressor built-in transform/dataset/metric to unify APIs cross different framework backends.

Refer to [dataset](./dataset.html), [transform](./transform.html), and [metric](./metric.html) to learn how to use them in yaml or code.