---
metadata:
  - name: generator
    content: Diplodoc Platform v5.52.0
alternate:
  - https://catboost.ai/docs/en/concepts/python-reference_catboostregressor_load_model.md
  - href: en/concepts/python-reference_catboostregressor_load_model.md
    type: text/markdown
    title: Markdown version
  - href: ../llms.txt
    type: text/markdown
    title: llms.txt
---
> **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt

# load_model

<!-- source: en/_includes/work_src/reusage/load_model--purpose.md -->
Load the model from a file.
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## Method call format {#call-format}

```
load_model(fname, format='cbm')
```

## Parameters {#parameters}

<!-- source: en/_includes/work_src/reusage/python__load_model__parameter.md -->
### fname

#### Description

The path to the input model.

**Possible types**

string

**Default value**

Required parameter

### format

#### Description

The format of the input model.

Possible values:
- cbm — CatBoost binary format.
- AppleCoreML(only datasets without categorical features are currently supported).
- json — JSON format. Refer to the [CatBoost JSON model tutorial](https://github.com/catboost/tutorials/blob/master/model_analysis/model_export_as_json_tutorial.ipynb) for format details.
- onnx — ONNX-ML format (only datasets without categorical features are currently supported). Refer to [https://onnx.ai/](https://onnx.ai/) for details. See the [ONNX](https://catboost.ai/docs/en/concepts/apply-onnx-ml.md) section for details on applying the resulting model.
- CpuSnapshot — CatBoost training snapshot format (only CPU and datasets without categorical features are currently supported).

**Possible types**

string

**Default value**

cbm
<!-- endsource: en/_includes/work_src/reusage/python__load_model__parameter.md -->


## Usage examples {#usage-examples}

```python
from catboost import CatBoostClassifier, Pool

train_data = [[1, 3],
              [0, 4],
              [1, 7]]
train_labels = [1, 0, 1]

# catboost_pool = Pool(train_data, train_labels)

model = CatBoostClassifier(learning_rate=0.03)
model.fit(train_data,
          train_labels,
          verbose=False)

model.save_model("model")

from_file = CatBoostClassifier()

from_file.load_model("model")

```