
---

## **1. Auto Classes Overview**
Auto Classes automatically determine the correct model, tokenizer, or configuration based on the provided model name or path. The primary Auto Classes include:

- `AutoModel`
- `AutoModelForSequenceClassification`
- `AutoModelForTokenClassification`
- `AutoModelForQuestionAnswering`
- `AutoModelForSeq2SeqLM`
- `AutoModelForCausalLM`
- `AutoModelForMaskedLM`
- `AutoTokenizer`
- `AutoConfig`

---

## **2. Common Methods in Auto Classes**
Each Auto Class provides several key methods for working with models:

### **Loading Pretrained Models and Tokenizers**
| Method | Description |
|--------|-------------|
| `.from_pretrained(model_name_or_path)` | Loads a model or tokenizer from a pretrained checkpoint. |
| `.from_config(config)` | Instantiates a model from a configuration object instead of a pretrained checkpoint. |
| `.from_model_type(model_type, config)` | Instantiates a model given a model type string and configuration. |
| `.from_pretrained(tokenizer_name_or_path, use_fast=True/False)` | Loads a tokenizer (fast/slow implementation). |

---

### **Saving Models and Tokenizers**
| Method | Description |
|--------|-------------|
| `.save_pretrained(save_directory)` | Saves the model/tokenizer configuration and weights locally. |
| `.push_to_hub(repo_name)` | Uploads a model or tokenizer to the Hugging Face Hub. |

---

### **Extending Auto Classes with Custom Models**
| Method | Description |
|--------|-------------|
| `.register(model_type, custom_class)` | Registers a custom model or configuration so it can be used with `AutoModel` or `AutoConfig`. |
| `.register_for_auto_class(cls)` | Registers a class to be used with Auto Classes automatically. |

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### **Converting Between Model Types**
| Method | Description |
|--------|-------------|
| `.to(device)` | Moves the model to a specified device (`cpu`, `cuda`, etc.). |
| `.half()` | Converts model weights to half precision (float16). |
| `.float()` | Converts model weights to full precision (float32). |
| `.bfloat16()` | Converts model weights to `bfloat16` precision. |

---

### **Configuration-Specific Methods**
| Method | Description |
|--------|-------------|
| `.get_config_dict(model_name_or_path)` | Retrieves the configuration dictionary of a pretrained model. |
| `.from_dict(config_dict)` | Loads a configuration object from a Python dictionary. |
| `.to_dict()` | Converts a configuration object into a dictionary. |

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### **Tokenizer-Specific Methods**
| Method | Description |
|--------|-------------|
| `.from_pretrained(tokenizer_name)` | Loads a tokenizer from a pretrained checkpoint. |
| `.encode(text, add_special_tokens=True)` | Converts text into token IDs. |
| `.decode(token_ids, skip_special_tokens=True)` | Converts token IDs back into text. |
| `.batch_encode_plus(text_list, padding=True, truncation=True, return_tensors="pt")` | Tokenizes multiple inputs efficiently. |

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## **3. Example Usage**
### **Loading a Pretrained Model**
```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("bert-base-cased")
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
```

### **Saving and Reloading a Model Locally**
```python
model.save_pretrained("./saved_model")
model = AutoModel.from_pretrained("./saved_model")
```

### **Registering a Custom Model**
```python
from transformers import AutoConfig, AutoModel

AutoConfig.register("my-custom-model", MyCustomConfig)
AutoModel.register(MyCustomConfig, MyCustomModel)
```

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