### Query Documentation Dynamically

Source: https://zjunlp.gitbook.io/easyedit/installation

Perform an HTTP GET request to the installation page with an 'ask' query parameter to get dynamic answers to your questions. The question should be specific and in natural language.

```http
GET https://zjunlp.gitbook.io/easyedit/installation.md?ask=<question>
```

--------------------------------

### Example: Initialize HyperParams using YAML

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/hparams

This example demonstrates how to initialize HyperParams using the `from_hparams` static method with a YAML configuration file for the MEMIT algorithm.

```python
hparams = MEMITHyperParams.from_hparams('./hparams/MEMIT/llama-7b.yaml')

```

--------------------------------

### Example Usage

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/hparams

Example demonstrating how to initialize HyperParams using the `from_hparams` method with a YAML configuration file.

```APIDOC
## Example

```python
hparams = MEMITHyperParams.from_hparams('./hparams/MEMIT/llama-7b.yaml')
```

### Request Body Example (YAML)

```yaml
alg_name: "MEMIT"
model_name: "./hugging_cache/llama-2-7b"
device: 0
layers: [4, 5, 6, 7, 8]
clamp_norm_factor: 4
layer_selection: "all"
fact_token: "subject_last"
v_num_grad_steps: 25
v_lr: 5e-1
v_loss_layer: 31
v_weight_decay: 1e-3
kl_factor: 0.0625
mom2_adjustment: true
mom2_update_weight: 15000
rewrite_module_tmp: "model.layers.{}.mlp.down_proj"
layer_module_tmp: "model.layers.{}"
mlp_module_tmp: "model.layers.{}.mlp"
attn_module_tmp: "model.layers.{}.self_attn"
ln_f_module: "model.norm"
lm_head_module: "lm_head"
mom2_dataset: "wikipedia"
mom2_n_samples: 100000
mom2_dtype: "float32"
```
```

--------------------------------

### Pull EasyEdit Docker Image

Source: https://zjunlp.gitbook.io/easyedit/installation

Download the pre-built EasyEdit Docker image from Docker Hub or Aliyun. This is a convenient way to get started without local environment setup.

```bash
docker pull nipelements/easyedit
```

```bash
docker pull registry.cn-hangzhou.aliyuncs.com/zjunlp/easyedit:v1
```

--------------------------------

### Query Documentation API Example

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/evaluate

To get additional information not directly on the page, make a GET request to the page URL with an 'ask' query parameter containing your question.

```http
GET https://zjunlp.gitbook.io/easyedit/easyeditor/evaluate.md?ask=<question>
```

--------------------------------

### Example YAML Configuration

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/hparams

A sample YAML configuration file for hyperparameters, specifying algorithm name, model path, device, layer selection, and various training parameters.

```yaml
alg_name: "MEMIT"
model_name: "./hugging_cache/llama-2-7b"
device: 0
layers: [4, 5, 6, 7, 8]
clamp_norm_factor: 4
layer_selection: "all"
fact_token: "subject_last"
v_num_grad_steps: 25
v_lr: 5e-1
v_loss_layer: 31
v_weight_decay: 1e-3
kl_factor: 0.0625
mom2_adjustment: true
mom2_update_weight: 15000
rewrite_module_tmp: "model.layers.{}.mlp.down_proj"
layer_module_tmp: "model.layers.{}"
mlp_module_tmp: "model.layers.{}.mlp"
attn_module_tmp: "model.layers.{}.self_attn"
ln_f_module: "model.norm"
lm_head_module: "lm_head"
mom2_dataset: "wikipedia"
mom2_n_samples: 100000
mom2_dtype: "float32"

```

--------------------------------

### Query Documentation via HTTP GET

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/editor

Demonstrates how to perform an HTTP GET request to the documentation URL with an 'ask' query parameter to retrieve specific information dynamically. The question should be self-contained and in natural language.

```http
GET https://zjunlp.gitbook.io/easyedit/easyeditor/editor.md?ask=<question>
```

--------------------------------

### Install EasyEdit using Pip

Source: https://zjunlp.gitbook.io/easyedit/installation

Clone the repository, create a conda environment with Python 3.9+, and install dependencies. Ensure you are using Python 3.9 or newer.

```shell
git clone https://github.com/zjunlp/EasyEdit.git
conda create -n EasyEdit python=3.9.7
...
pip install -r requirements.txt
```

--------------------------------

### Query Documentation via GET Request

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/mend

Shows how to query the documentation dynamically by appending an 'ask' query parameter to the page URL.

```http
GET https://zjunlp.gitbook.io/easyedit/easyeditor/models/mend.md?ask=<question>
```

--------------------------------

### Example Usage of Serac Editor

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/serac

Demonstrates how to initialize Serac hyperparameters, create an editor instance, and apply edits to a model using a list of prompts and their corresponding new targets.

```python
hparams = SERACHyperaParams.from_hparams("llama-7b.yaml")
editor = BaseEditor.from_hparams(hparams)
prompts = ['What university did Watts Humphrey attend?',
    'Which family does Ramalinaceae belong to',
    'What role does Denny Herzig play in football?'
]
target_new = ['University of Michigan',
    'Lamiinae',
    'winger'
]
metrics, edited_model, _ = editor.edit(
    prompts=prompts,
    target_new=target_new,
    keep_original_weight=True
)
```

--------------------------------

### Agent Instructions: Querying Documentation

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/rome

Provides instructions on how to query the documentation dynamically using a GET request with an 'ask' query parameter. This is useful for retrieving information not explicitly present on the current page.

```http
GET https://zjunlp.gitbook.io/easyedit/easyeditor/models/rome.md?ask=<question>
```

--------------------------------

### Query Documentation Dynamically

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/trainer

Perform an HTTP GET request with the `ask` query parameter to retrieve specific information or clarifications from the documentation. Use this when the answer is not explicitly present or for additional context.

```http
GET https://zjunlp.gitbook.io/easyedit/easyeditor/trainer.md?ask=<question>
```

--------------------------------

### Query Documentation Dynamically

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models

Use this GET request to query the documentation dynamically when information is not explicitly present. The question should be specific and in natural language.

```http
GET https://zjunlp.gitbook.io/easyedit/easyeditor/models.md?ask=<question>
```

--------------------------------

### Example of Applying MEND Edits

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/mend

Demonstrates how to load MEND hyperparameters, initialize an editor, and apply edits to a model with specified prompts and targets.

```python
// ...
hparams = MENDHyperaParams.from_hparams("llama-7b.yaml")
editor = BaseEditor.from_hparams(hparams)
prompts = ['What university did Watts Humphrey attend?',
    'Which family does Ramalinaceae belong to',
    'What role does Denny Herzig play in football?'
<strong>]
</strong>target_new = ['University of Michigan',
    'Lamiinae',
    'winger'
]
metrics, edited_model, _ = editor.edit(
    prompts=prompts,
    target_new=target_new,
    keep_original_weight=True
)

```

--------------------------------

### Example: Editing Model Prompts with MEMIT

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/memit

This example demonstrates how to use the MEMIT editor to modify model responses for a list of prompts. It initializes hyperparameters, creates an editor instance, and then applies edits to change target outputs.

```python
// ...
hparams = MEMITHyperaParams.from_hparams("llama-7b.yaml")
editor = BaseEditor.from_hparams(hparams)
prompts = ['What university did Watts Humphrey attend?',
    'Which family does Ramalinaceae belong to',
    'What role does Denny Herzig play in football?'
<strong>]
</strong>target_new = ['University of Michigan',
    'Lamiinae',
    'winger'
]
metrics, edited_model, _ = editor.edit(
    prompts=prompts,
    target_new=target_new,
    keep_original_weight=True
)

```

--------------------------------

### Example: Editing Model with IKE

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/ike

Demonstrates how to use the IKE editor to modify a pre-trained model. It involves loading hyperparameters, initializing the editor, defining prompts and their new targets, and then applying the edits.

```python
// ...
hparams = IKEHyperaParams.from_hparams("llama-7b.yaml")
editor = BaseEditor.from_hparams(hparams)
prompts = ['What university did Watts Humphrey attend?',
    'Which family does Ramalinaceae belong to',
    'What role does Denny Herzig play in football?'
<strong>]
</strong>target_new = ['University of Michigan',
    'Lamiinae',
    'winger'
]
metrics, edited_model, _ = editor.edit(
    prompts=prompts,
    target_new=target_new,
    keep_original_weight=True
)

```

--------------------------------

### ROME Model Editing Example

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/rome

Demonstrates how to use the ROME editor to modify a language model. It involves loading hyperparameters, initializing the editor, defining prompts and new targets, and then applying the edits.

```python
hparams = ROMEHyperaParams.from_hparams("llama-7b.yaml")
editor = BaseEditor.from_hparams(hparams)
prompts = ['What university did Watts Humphrey attend?',
    'Which family does Ramalinaceae belong to',
    'What role does Denny Herzig play in football?'
]
target_new = ['University of Michigan',
    'Lamiinae',
    'winger'
]
metrics, edited_model, _ = editor.edit(
    prompts=prompts,
    target_new=target_new,
    keep_original_weight=True
)
```

--------------------------------

### Example Evaluation Metrics

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/evaluate

This JSON structure represents the expected output format for evaluation metrics, including rewrite accuracy, rephrase accuracy, locality, and portability, for both pre- and post-editing states.

```json
{
    "post": {
        "rewrite_acc": ,
        "rephrase_acc": ,
        "locality": {
            "YOUR_LOCALITY_KEY": ,
            //...
        },
        "portablility": {
            "YOUR_PORTABILITY_KEY": ,
            //...
        },
    },
    "pre": {
        "rewrite_acc": ,
        "rephrase_acc": ,
        "portablility": {
            "YOUR_PORTABILITY_KEY": ,
            //...
        },
    }
}
```

--------------------------------

### Perform Batch Fact Editing with batch_edit()

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/editor

Main function for batch fact editing. Accepts lists for 'prompts', 'target_new', and optional 'ground_truth'. Useful for applying edits to multiple examples simultaneously.

```python
def batch_edit(self,
    prompts: List[str],
    target_new: List[str],
    ground_truth: Optional[List[str]] = None,
    rephrase_prompts: Optional[List[str]] = None,
    locality_prompts: Optional[List[str]] = None,
    locality_ground_truth: Optional[List[str]] = None,
    keep_original_weight=False,
    verbose=True,
    **kwargs
    )
```

--------------------------------

### Initialize BaseEditor with Hyperparameters

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/editor

Static method to initialize the BaseEditor using provided hyperparameters. Ensure HyperParams are correctly configured before use.

```python
def from_hparams(cls, hparams: HyperParams):
    return cls(hparams)
```

--------------------------------

### Build and Run EasyEdit Docker Image Locally

Source: https://zjunlp.gitbook.io/easyedit/installation

Clone the EasyEdit repository, build a Docker image locally, and then run it as a container, mapping port 8080. This method is useful for development or customization.

```bash
git clone https://github.com/zjunlp/EasyEdit.git
cd EasyEdit
docker build -t your-image-name .
```

```bash
docker run -p 8080:80 your-image-name
```

--------------------------------

### Initialize HyperParams from JSON

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/hparams

Use this static method to initialize hyperparameters from a JSON configuration file path. It takes the file path as input and returns a Hyperparams object.

```python
def from_json(cls, fpath: str):

```

--------------------------------

### Initialize HyperParams from YAML

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/hparams

Use this static method to initialize hyperparameters from a YAML configuration file path or name. It takes the YAML path or name as input and returns a Hyperparams object.

```python
def from_hparams(cls, hparams_name_or_path: str):

```

--------------------------------

### Initialize MendRewriteExecutor

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/mend

Initializes the MendRewriteExecutor with a pre-trained model, tokenizer, and MEND hyperparameters.

```python
def init_model(self, model, tok, params: MENDHyperParams)
```

--------------------------------

### HyperParams Initialization from YAML

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/hparams

Initializes HyperParams object from a YAML configuration file. This is a static method.

```APIDOC
## from_hparams()

### Description
Static method, hyperparams can be initialized through `yaml`.

### Method Signature
```python
def from_hparams(cls, hparams_name_or_path: str):
```

### Parameters
#### Path Parameters
- **hparams_name_or_path** (Str) - Required - yaml config path for editing

### Return Type
- **hparams** (Hyperparams) - The hyperparams class for editing
```

--------------------------------

### HyperParams Initialization from JSON

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/hparams

Initializes HyperParams object from a JSON configuration file. This is a static method.

```APIDOC
## from_json()

### Description
Static method, hyperparams can be initialized through `json`.

### Method Signature
```python
def from_json(cls, fpath: str):
```

### Parameters
#### Path Parameters
- **fpath** (Str) - Required - config path for editing

### Return Type
- **hparams** (Hyperparams) - The hyperparams class for editing
```

--------------------------------

### Initialize Serac Model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/serac

Loads the trained MEND model for Serac editing. Requires the model, tokenizer, and Serac-specific hyperparameters.

```python
init_model(self, model, tok, params: SERACHparams)
```

--------------------------------

### Edit Model with EasyEditor

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/editor

Initializes the MEND model hyperparameters and the BaseEditor, then performs an edit operation with specified prompts and ground truth. Ensure MENDHyperParams and BaseEditor are imported.

```python
hparams = MENDHyperParams.from_hparams('./hparams/MEND/gpt2-xl')
editor = BaseEditor.from_hparams(hparams)
metrics, edited_model, weight_copy= editor.edit(
        prompts='What university did Watts Humphrey attend?' if prompts is None else prompts,
        ground_truth='Illinois Institute of Technology' if ground_truth is None else ground_truth,
        target_new='University of Michigan' if target_new is None else target_new,
        keep_original_weight=True,
)
```

--------------------------------

### BaseEditor.from_hparams

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/editor

Initializes a BaseEditor instance using hyperparameters. This is a static method used for creating editor objects.

```APIDOC
## BaseEditor.from_hparams()

### Description
Static method to initialize an editor instance through hyperparameters.

### Method Signature
`from_hparams(cls, hparams: HyperParams) -> BaseEditor`

### Parameters
* **hparams** (HyperParams): Hyperparameters for the editing method.

### Return Type
* **editor** (BaseEditor): The Editor class defined by `hparams`.
```

--------------------------------

### SeracRewriterExecutor.init_model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/serac

Loads the trained MEND model for SERAC editing.

```APIDOC
## init_model

### Description
Loads the trained MEND model.

### Method Signature
`init_model(self, model, tok, params: SERACHparams)`

### Parameters
* **model** (PreTrainedModel) - The model to be edited.
* **tok** (PreTrainedTokenizer) - The tokenizer for inputs.
* **params** (SERACHparams) - Hyperparameters for the editing method.
```

--------------------------------

### MendRewriteExecutor.init_model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/mend

Initializes the MEND model by setting up the necessary components for applying edits. It requires a pre-trained model, its tokenizer, and MEND-specific hyperparameters.

```APIDOC
## init_model()

### Description
Initializes the MEND model. Requires a pre-trained specific model structure and weights (use Trainer).

### Method
`init_model(self, model, tok, params: MENDHyperParams)`

### Parameters
* **model** (PreTrainedModel) - The model to be edited.
* **tok** (PreTrainedTokenizer) - The tokenizer for inputs.
* **params** (MENDHyperParams) - Hyperparameters for the editing method.
```

--------------------------------

### Execute ROME Update

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/rome

Executes the ROME update algorithm for a specified edit request. It takes the model, tokenizer, request details, and hyperparameters as input, returning the computed delta weights.

```python
def execute_rome(
    model: AutoModelForCausalLM,
    tok: AutoTokenizer,
    request: Dict,
    hparams: ROMEHyperParams,
) -> Dict[str, Tuple[torch.Tensor]]:
```

--------------------------------

### Encode IKE Facts with SentenceTransformer

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/ike

Encodes sentences from a development dataset using a SentenceTransformer model to prepare them for in-context demonstration retrieval. The resulting dense embeddings are stored in pickle format.

```python
def encode_ike_facts(
    sentence_model: SentenceTransformer,
    ds: Dataset,
    hparams: IKEHyperParams
):

```

--------------------------------

### Perform Fact Editing with edit()

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/editor

Main function for single-instance fact editing. Use 'prompts' and 'target_new' to define the edit. 'ground_truth' can be optional. Consider 'keep_original_weight' for sequential editing.

```python
def edit(self,
    prompts: Union[str, List[str]],
    target_new: Union[str, List[str]],
    ground_truth: Optional[Union[str, List[str]]],
    rephrase_prompts: Optional[Union[str, List[str]]] = None,
    locality_inputs:  Optional[Dict] = None,
    portability_inputs: Optional[Dict] = None,
    keep_original_weight=False,
    verbose=True,
    **kwargs
    )
```

--------------------------------

### Apply MEMIT to Model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/memit

This is the main function to apply MEMIT to a model. It takes the model, tokenizer, requests, and hyperparameters, and returns the edited model weights. The `keep_original_weight` parameter controls sequential editing behavior.

```python
def apply_memit_to_model(
    self,
    model: AutoModelForCausalLM,
    tok: AutoTokenizer,
    requests: List[Dict],
    hparams: MEMITHyperParams,
    copy=False,
    return_orig_weights=False,
    keep_original_weight=False,
    **kwargs
):
```

--------------------------------

### Execute MEMIT Update Algorithm

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/memit

This function executes the MEMIT update algorithm for a specified edit request and hyperparameters. It returns the new delta weights for the model.

```python
def execute_memit(
    model: AutoModelForCausalLM,
    tok: AutoTokenizer,
    request: List[Dict],
    hparams: MEMITHyperParams,
) -> Dict[str, Tuple[torch.Tensor]]:
```

--------------------------------

### execute_memit

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/memit

Executes the MEMIT update algorithm for specified edits and layers.

```APIDOC
## execute_memit()

### Description
Execute the MEMIT update algorithm for the specified update at the specified layers.

### Parameters
* **model** (PreTrainedModel): The model to be edited.
* **tok** (PreTrainedTokenizer): The tokenizer for inputs.
* **requests** (List[Dict]): The edit descriptors and targets.
* **hparams** (Hyperparams): Hyperparameters for the editing method.

### Return Type
* **delta** (Dict[str, Tuple[torch.Tensor]]): New delta weights.
```

--------------------------------

### compute_locality_quality

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/evaluate

Computes the locality quality. The input-output format is the same as `compute_rewrite_or_rephrase_quality`. It compares whether the output tokens before and after editing are the same and calculates the average accuracy rate.

```APIDOC
## compute_locality_quality()

### Description
Computes the locality quality. The input-output format is the same as `compute_rewrite_or_rephrase_quality`.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **model** (PreTrainedModel): model to be edited
* **model_name** (Str): model_name_or_path
* **hparams** (HyperParams): hyperparameters for editing method
* **tok** (PreTrainedTokenizer): tokenizer for inputs
* **prompt** (Str): the edit descriptor
* **target_new** (Str): the edit target
* **device** (Any): the device to run the computation on
* **test_rephrase** (bool): whether to evalute the rephrase prompt (For Generalization)

### Return Type
* metrics (Dict): model weights after editing

### Example
```json
{
    "post": {
        "rewrite_acc": ,
        "rephrase_acc": ,
        "locality": {
            "YOUR_LOCALITY_KEY": ,
            //...
        },
        "portablility": {
            "YOUR_PORTABILITY_KEY": ,
            //...
        },
    },
    "pre": {
        "rewrite_acc": ,
        "rephrase_acc": ,
        "portablility": {
            "YOUR_PORTABILITY_KEY": ,
            //...
        },
    }
}
```
```

--------------------------------

### Compute Locality Quality Function Signature

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/evaluate

The input-output format for this function is the same as `compute_rewrite_or_rephrase_quality`. It calculates average accuracy by comparing output tokens before and after editing.

```python
def compute_locality_quality() -> Dict:
```

--------------------------------

### Compute Rewrite or Rephrase Quality Function Signature

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/evaluate

Use this function for evaluating 'Reliability' and 'Generalization'. It requires model, tokenizer, and editing hyperparameters.

```python
def compute_rewrite_or_rephrase_quality(
    model,
    model_name,
    hparams: HyperParams,
    tok: AutoTokenizer,
    prompt: str,
    target_new: str,
    device,
    test_rephrase: bool = False
) -> typing.Dict:
```

--------------------------------

### Apply IKE to a Pre-Trained Model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/ike

Applies the IKE editing method to a pre-trained causal language model. This function takes a model, tokenizer, edit requests, and hyperparameters to modify the model's behavior based on in-context learning.

```python
def apply_ike_to_model(
    self,
    model: AutoModelForCausalLM,
    tok: AutoTokenizer,
    requests: List[Dict],
    hparams: IKEHyperParams,
    copy=False,
    return_orig_weights=False,
    keep_original_weight=False,
    **kwargs
):

```

--------------------------------

### BaseEditor.batch_edit

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/editor

Performs fact editing on the model for multiple prompts and targets in a batch. Returns metrics and the edited model.

```APIDOC
## BaseEditor.batch_edit()

### Description
Main function to perform fact editing for multiple inputs according to the selected editing method. It returns editing metrics and the edited model.

### Method Signature
`batch_edit(self, prompts: List[str], target_new: List[str], ground_truth: Optional[List[str]] = None, rephrase_prompts: Optional[List[str]] = None, locality_prompts: Optional[List[str]] = None, locality_ground_truth: Optional[List[str]] = None, keep_original_weight: bool = False, verbose: bool = True, **kwargs) -> List[Dict]`

### Parameters
* **prompts** (List[str]): A list of prompt strings for the edit descriptors.
* **target_new** (List[str]): A list of prompt strings for the edit targets.
* **ground_truth** (Optional[List[str]]): A list of original model outputs for the edit descriptors. Can be set to `None`.
* **rephrase_prompts** (Optional[List[str]]): A list of rephrased prompt strings, semantically similar to `prompts`, used to test generalization.
* **locality_prompts** (Optional[List[str]]): A list of prompts for testing locality.
* **locality_ground_truth** (Optional[List[str]]): A list of ground truth values corresponding to `locality_prompts`.
* **keep_original_weight** (bool): Determines if edits are sequential. `False` edits sequentially (original weight not maintained), `True` does not.
* **verbose** (bool): If `True`, prints intermediate output.

### Return Type
* **metrics** (List[Dict]): Metrics for model editing. Refer to the provided link for details.
* **edited_model** (PreTrainedModel): Model weights after editing.
```

--------------------------------

### apply_memit_to_model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/memit

Applies MEMIT to a model given a request, returning the changed model weights.

```APIDOC
## apply_memit_to_model()

### Description
Given the request, it applies MEMIT to your model. Returns the changed weights of the model.

### Parameters
* **self**
* **model** (PreTrainedModel): The model to be edited.
* **tok** (PreTrainedTokenizer): The tokenizer for inputs.
* **requests** (List[Dict]): The edit descriptors and targets.
* **hparams** (MEMITHyperParams): Hyperparameters for the editing method.
* **copy** (bool, optional): Whether to copy the original model. Defaults to False.
* **return_orig_weights** (bool, optional): Whether to return the weights of the original model. Defaults to False.
* **keep_original_weight** (bool, optional): Whether to edit sequentially. Defaults to False.
    * `False`: edit sequentially (because the original weight is not maintained after each edit).
    * `True`: not edit sequentially.
* **kwargs

### Return Type
* **edited_model** (PreTrainedModel): Model weights after editing.
```

--------------------------------

### apply_ike_to_model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/ike

Applies IKE edits to a pre-trained causal language model using provided requests and hyperparameters.

```APIDOC
## apply_ike_to_model

### Description
Main function to apply IKE to a model. Utilizes the preceding prompt to modify the behavior of the model.

### Method
`apply_ike_to_model(self, model: AutoModelForCausalLM, tok: AutoTokenizer, requests: List[Dict], hparams: IKEHyperParams, copy=False, return_orig_weights=False, keep_original_weight=False, **kwargs)`

### Parameters
#### Parameters
- **model** (PreTrainedModel) - Required - The model to be edited.
- **tok** (PreTrainedTokenizer) - Required - The tokenizer for inputs.
- **requests** (List[Dict]) - Required - The edit descriptors and targets.
- **hparams** (Hyperparams) - Required - Hyperparameters for the editing method.
- **copy** (bool) - Optional - Whether to copy the original model.
- **return_orig_weights** (bool) - Optional - Whether to return the weights of the original model.
- **keep_original_weight** (bool) - Optional - Whether to edit sequentially. `False` edits sequentially; `True` does not edit sequentially.

### Returns
- **edited_model** (PreTrainedModel) - The model weights after editing.
```

--------------------------------

### Apply ROME to Model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/rome

Applies the ROME editing algorithm to a given model based on a list of requests. This function modifies the model's weights and can optionally return original weights or maintain sequential editing.

```python
def apply_rome_to_model(
    self,
    model: AutoModelForCausalLM,
    tok: AutoTokenizer,
    requests: List[Dict],
    hparams: ROMEHyperParams,
    copy=False,
    return_orig_weights=False,
    keep_original_weight=False,
    **kwargs
):
```

--------------------------------

### BaseEditor.edit

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/editor

Performs fact editing on the model based on the provided prompts and target. It returns metrics and the edited model weights.

```APIDOC
## BaseEditor.edit()

### Description
Main function to perform fact editing according to the selected editing method. It returns editing metrics and the edited model.

### Method Signature
`edit(self, prompts: Union[str, List[str]], target_new: Union[str, List[str]], ground_truth: Optional[Union[str, List[str]]] = None, rephrase_prompts: Optional[Union[str, List[str]]] = None, locality_inputs: Optional[Dict] = None, portability_inputs: Optional[Dict] = None, keep_original_weight: bool = False, verbose: bool = True, **kwargs) -> List[Dict]`

### Parameters
* **prompts** (Union[str, List[str]]): The prompt string of the edit descriptor.
* **target_new** (Union[str, List[str]]): The prompt string of the edit target.
* **ground_truth** (Optional[Union[str, List[str]]]): The original model output of the edit descriptor. Can be set to `None`.
* **rephrase_prompts** (Optional[Union[str, List[str]]]): A rephrased prompt string, semantically similar to `prompts`, used to test generalization.
* **locality_inputs** (Optional[Dict]): Dictionary containing prompts and their corresponding ground truths to test locality.
* **portability_inputs** (Optional[Dict]): Dictionary similar to `locality_inputs`, used to test portability.
* **keep_original_weight** (bool): Determines if edits are sequential. `False` edits sequentially (original weight not maintained), `True` does not.
* **verbose** (bool): If `True`, prints intermediate output.

### Return Type
* **metrics** (List[Dict]): Metrics for model editing. Refer to the provided link for details.
* **edited_model** (PreTrainedModel): Model weights after editing.
```

--------------------------------

### apply_rome_to_model()

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/rome

Applies the ROME editing algorithm to a given model based on a list of requests. This function returns the modified model weights.

```APIDOC
## apply_rome_to_model()

### Description
Given the request, it applies ROME to your model. Return the changed weights of the model.

### Signature
```python
def apply_rome_to_model(
    self,
    model: AutoModelForCausalLM,
    tok: AutoTokenizer,
    requests: List[Dict],
    hparams: ROMEHyperParams,
    copy=False,
    return_orig_weights=False,
    keep_original_weight=False,
    **kwargs
):
```

### Parameters
* **model** (<mark style="color:purple;">PreTrainedModel</mark>): The model to be edited.
* **tok** (<mark style="color:purple;">PreTrainedTokenizer</mark>): The tokenizer for inputs.
* **requests** (<mark style="color:purple;">List[Dict]</mark>): The edit descriptors and targets.
* **hparams** (<mark style="color:purple;">Hyperparams</mark>): Hyperparameters for the editing method.
* **copy** (<mark style="color:purple;">bool</mark>): Whether to copy the original model.
* **return_orig_weights** (<mark style="color:purple;">bool</mark>): Whether to return the weights of the original model.
* **keep_original_weight** (<mark style="color:purple;">bool</mark>): Whether to edit sequentially.
    * `False`: Edit sequentially (because the original weight is not maintained after each edit).
    * `True`: Not edit sequentially.

### Return Type
* **edited_model** (<mark style="color:purple;">PreTrainedModel</mark>): Model weights after editing.
```

--------------------------------

### Apply Serac Edits to Model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/serac

Applies Serac edits to a given model based on a list of requests. Can optionally copy the original model and control the return of original weights.

```python
def apply_to_model(
    self,
    model: AutoModelForCausalLM,
    tok: AutoTokenizer,
    requests: List[Dict],
    hparams: SERACHparams,
    copy=False,
    return_orig_weights=False,
    keep_original_weight=False,
    **kwargs
):
```

--------------------------------

### Apply MEND Edits to Model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/mend

Applies MEND edits to a language model based on a list of requests. Can optionally return original weights or keep them after editing.

```python
def apply_to_model(
    self,
    model: AutoModelForCausalLM,
    tok: AutoTokenizer,
    requests: List[Dict],
    hparams: MENDHyperParams,
    copy=False,
    return_orig_weights=False,
    keep_original_weight=False,
    **kwargs
):
```

--------------------------------

### execute_rome()

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/rome

Executes the ROME update algorithm for a specified update at a given layer. This function modifies the model's weights based on the provided request and hyperparameters.

```APIDOC
## execute_rome()

### Description
Execute the ROME update algorithm for the specified update at the specified layer.

### Signature
```python
def execute_rome(
    model: AutoModelForCausalLM,
    tok: AutoTokenizer,
    request: Dict,
    hparams: ROMEHyperParams,
) -> Dict[str, Tuple[torch.Tensor]]:
```

### Parameters
* **model** (<mark style="color:purple;">PreTrainedModel</mark>): The model to be edited.
* **tok** (<mark style="color:purple;">PreTrainedTokenizer</mark>): The tokenizer for inputs.
* **requests** (<mark style="color:purple;">List[Dict]</mark>): The edit descriptors and targets.
* **hparams** (<mark style="color:purple;">Hyperparams</mark>): Hyperparameters for the editing method.

### Return Type
* **delta** (<mark style="color:purple;">Dict</mark>[<mark style="color:purple;">str</mark>, Tuple[<mark style="color:purple;">torch.Tensor</mark>]>): New delta weights.
```

--------------------------------

### encode_ike_facts

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/ike

Encodes sentences from a dataset using a sentence transformer model to retrieve nearest neighbors for in-context demonstrations.

```APIDOC
## encode_ike_facts

### Description
Encodes samples in the dev set to retrieve nearest neighbors as in-context demonstrations.

### Parameters
#### Parameters
- **sentence_model** (SentenceTransformer) - Required - The model to encode demonstrations. Defaults to 'all-MiniLM-L6-v2'.
- **ds** (Dataset) - Required - The dev set, used as a corpus for retrieving demonstrations.
- **hparams** (Hyperparams) - Required - Hyperparameters for the editing method.

### Returns
Stores dense embeddings in the form of pickle.
```

--------------------------------

### SeracRewriterExecutor.apply_to_model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/serac

Main function to apply SERAC edits to a model based on provided requests and hyperparameters.

```APIDOC
## apply_to_model

### Description
Main function: Given the request, it applies SERAC to your model. Returns the changed weights of the model.

### Method Signature
`apply_to_model(self, model: AutoModelForCausalLM, tok: AutoTokenizer, requests: List[Dict], hparams: SERACHparams, copy=False, return_orig_weights=False, keep_original_weight=False, **kwargs)`

### Parameters
* **model** (AutoModelForCausalLM) - The model to be edited.
* **tok** (AutoTokenizer) - The tokenizer for inputs.
* **requests** (List[Dict]) - The edit descriptors and targets.
* **hparams** (SERACHparams) - Hyperparameters for editing method.
* **copy** (bool) - Whether to copy the original model. Defaults to `False`.
* **return_orig_weights** (bool) - Whether to return the weights of the original model. Defaults to `False`.
* **keep_original_weight** (bool) - Whether to edit sequentially. If `False`, edits sequentially as the original weight is not maintained after each edit. If `True`, does not edit sequentially. Defaults to `False`.

### Return Type
* **edited_model** (PreTrainedModel) - Model weights after editing.
```

--------------------------------

### compute_rewrite_or_rephrase_quality

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/evaluate

Evaluation method for Reliability and Generalization. It computes the token with the highest probability at each position and compares it with the ground truth to ascertain the mean accuracy of the probabilistic model.

```APIDOC
## compute_rewrite_or_rephrase_quality()

### Description
Evaluation method for `Reliability` and `Generalization`.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **model** (PreTrainedModel): model to be edited
* **model_name** (Str): model_name_or_path
* **hparams** (HyperParams): hyperparameters for editing method
* **tok** (PreTrainedTokenizer): tokenizer for inputs
* **prompt** (Str): the edit descriptor
* **target_new** (Str): the edit target
* **device** (Any): the device to run the computation on
* **test_rephrase** (bool): whether to evalute the rephrase prompt (For Generalization)

### Return Type
* metrics (Dict): model weights after editing
```

--------------------------------

### MendRewriteExecutor.apply_to_model

Source: https://zjunlp.gitbook.io/easyedit/easyeditor/models/mend

Applies MEND edits to the provided model. This is the main function for performing model editing, returning the modified model weights.

```APIDOC
## apply_to_model()

### Description
Main function: Given the request, it applies mend to your model. Returns the changed weights of the model.

### Method
`apply_to_model(self, model: AutoModelForCausalLM, tok: AutoTokenizer, requests: List[Dict], hparams: MENDHyperParams, copy=False, return_orig_weights=False, keep_original_weight=False, **kwargs)`

### Parameters
* **model** (AutoModelForCausalLM) - The model to be edited.
* **tok** (AutoTokenizer) - The tokenizer for inputs.
* **requests** (List[Dict]) - The edit descriptors and targets.
* **hparams** (MENDHyperParams) - Hyperparameters for the editing method.
* **copy** (bool) - Whether to copy the original model. Defaults to False.
* **return_orig_weights** (bool) - Whether to return the weights of the original model. Defaults to False.
* **keep_original_weight** (bool) - Whether to edit sequentially. Defaults to False.
    * `False`: edit sequentially (because the original weight is not maintained after each edit).
    * `True`: not edit sequentially.

### Return Type
* **edited_model** (PreTrainedModel) - Model weights after editing.
```

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