[docs] HfQuantizer (#28820)

* tidy

* fix path

docs/source/en/hf_quantizer.md

@@ -20,7 +20,6 @@ Transformers supports and integrates many quantization methods such as QLoRA, GP
 
 This guide will show you how to integrate a new quantization method with the [`HfQuantizer`] class.
 
-
 ## Requirements
 
 Before integrating a new quantization method into Transformers, ensure the method you are trying to add meets the following prerequisites. Only quantization methods that can be run with PyTorch modules are currently supported.
@@ -37,7 +36,9 @@ class Linear4bit(nn.Module):
     def forward(self, x):
         return my_4bit_kernel(x, self.weight, self.bias)
 ```
+
 This way, Transformers models can be easily quantized by replacing some instances of `nn.Linear` with a target class.
+
 - The quantization method should be serializable. You can save the quantized weights locally or push them to the Hub.
 - Make sure the package that contains the quantization kernels/primitive is stable (no frequent breaking changes).
 
@@ -45,26 +46,24 @@ For some quantization methods, they may require "pre-quantizing" the models thro
 
 ## Build a new HFQuantizer class
 
-1. 📕 Create a new quantization config class inside `src/transformers/utils/quantization_config.py` and make sure to expose the new quantization config inside Transformers main `init` by adding it to the `_import_structure` object of `src/transformers/__init__.py`.
+1. Create a new quantization config class inside [src/transformers/utils/quantization_config.py](https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/src/transformers/utils/quantization_config.py) and make sure to expose the new quantization config inside Transformers main `init` by adding it to the [`_import_structure`](https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/src/transformers/__init__.py#L1088) object of [src/transformers/__init__.py](https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/src/transformers/__init__.py).
 
-2-  🗃 Create a new file inside `src/transformers/quantizers/` named `quantizer_your_method.py`, and make it inherit from `src/transformers/quantizers/base.py::HfQuantizer`. Make sure to add the new quantizer and quantization config in the quantization auto-mapping in `src/transformers/quantizers/auto.py`
+2. Create a new file inside [src/transformers/quantizers/](https://github.com/huggingface/transformers/tree/abbffc4525566a48a9733639797c812301218b83/src/transformers/quantizers) named `quantizer_your_method.py`, and make it inherit from [src/transformers/quantizers/base.py::HfQuantizer](https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/src/transformers/quantizers/base.py#L28). Make sure to add the new quantizer and quantization config in the quantization auto-mapping in [src/transformers/quantizers/auto.py](https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/src/transformers/quantizers/auto.py).
 
-3- 🔩 Define the following class attributes/property methods for your quantization method:
+3. Define the following class attributes/property methods for your quantization method:
 
 * `requires_calibration`: Whether the quantization method requires a data calibration process. If set to `True`, you can only support inference (with quantized weights) and not inference and quantization.
-* `required_packages`: A list of strings of the required packages to use the quantized weights. You might need to define some new utility methods such as `is_auto_awq_available` in `transformers/src/utils/import_utils.py`.
+* `required_packages`: A list of strings of the required packages to use the quantized weights. You might need to define some new utility methods such as `is_auto_awq_available` in [transformers/src/utils/import_utils.py](https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/src/transformers/utils/import_utils.py).
 * `requires_parameters_quantization`: Only required if your quantization method requires extra attention to the underlying `nn.Parameter` object. For example, bitsandbytes uses `Params4bit` and `Int8Param`, which requires some extra attention when quantizing the model. Most of the recent quantization method packs int2/int4 weights inside `torch.uint8` weights, so this flag should not be really required (set to `False` by default).
 * `is_serializable`: A property method to determine whether the method is serializable or not.
 * `is_trainable`:  A property method to determine whether you can fine-tune models on top of the quantization method (with or without PEFT approaches).
 
+4. Write the `validate_environment` and `update_torch_dtype` methods. These methods are called before creating the quantized model to ensure users use the right configuration. You can have a look at how this is done on other quantizers.
 
-4- 🪛 Write the `validate_environment` and `update_torch_dtype` methods. These methods are called before creating the quantized model to ensure users use the right configuration. You can have a look at how this is done on other quantizers.
+5. Write the `_process_model_before_weight_loading` method. In Transformers, the quantized models are initialized first on the `"meta"` device before loading the weights. This means the `_process_model_before_weight_loading` method takes care of manipulating the model skeleton to replace some modules (e.g., `nn.Linear`) with the target modules (quantization modules). You can define a module replacement logic or any other utility method by creating a new file in [transformers/src/integrations/](https://github.com/huggingface/transformers/tree/abbffc4525566a48a9733639797c812301218b83/src/transformers/integrations) and exposing the relevant methods in that folder's `__init__.py` file. The best starting point would be to have a look at another quantization methods such as [quantizer_awq.py](https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/src/transformers/quantizers/quantizer_awq.py).
 
-5- 🖋 Write the `_process_model_before_weight_loading` method. In Transformers, the quantized models are initialized first on the `"meta"` device before loading the weights. This means the `_process_model_before_weight_loading` method takes care of manipulating the model skeleton to replace some modules (e.g., `nn.Linear`) with the target modules (quantization modules). You can define a module replacement logic or any other utility method by creating a new file in `transformers/src/integrations/` and exposing the relevant methods in that folder's `__init__.py` file. The best starting point would be to have a look at another quantization methods such as `quantizer_awq.py`
+6. Write the `_process_model_after_weight_loading` method. This method enables implementing additional features that require manipulating the model after loading the weights.
 
-6- 🖊 Write the `_process_model_after_weight_loading` method. This method enables implementing additional features that require manipulating the model after loading the weights.
-
-7- 📖 Document everything! Make sure your quantization method is documented in the `docs/source/en/quantization.md` file.
-
-8- 🟢 Add tests! You should add tests by first adding the package in our nightly Dockerfile inside `docker/transformers-all-latest-gpu` and then adding a new test file in `tests/quantization/xxx`. Feel free to check out how it is implemented for other quantization methods.
+7. Document everything! Make sure your quantization method is documented in the [`docs/source/en/quantization.md`](https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/docs/source/en/quantization.md) file.
 
+8. Add tests! You should add tests by first adding the package in our nightly Dockerfile inside `docker/transformers-all-latest-gpu` and then adding a new test file in `tests/quantization/xxx`. Feel free to check out how it is implemented for other quantization methods.