Docs: fix `generate`-related rendering issues (#30600)

* does this work?

* like this?

* fix the other generate links

* missing these

docs/source/en/generation_strategies.md

@@ -21,7 +21,7 @@ more. It also plays a role in a variety of mixed-modality applications that have
 and vision-to-text. Some of the models that can generate text include
 GPT2, XLNet, OpenAI GPT, CTRL, TransformerXL, XLM, Bart, T5, GIT, Whisper.
 
-Check out a few examples that use [`~transformers.generation_utils.GenerationMixin.generate`] method to produce
+Check out a few examples that use [`~generation.GenerationMixin.generate`] method to produce
 text outputs for different tasks:
 * [Text summarization](./tasks/summarization#inference)
 * [Image captioning](./model_doc/git#transformers.GitForCausalLM.forward.example)

docs/source/en/tasks/language_modeling.md

@@ -382,7 +382,7 @@ Tokenize the text and return the `input_ids` as PyTorch tensors:
 >>> inputs = tokenizer(prompt, return_tensors="pt").input_ids
 ```
 
-Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to generate text.
+Use the [`~generation.GenerationMixin.generate`] method to generate text.
 For more details about the different text generation strategies and parameters for controlling generation, check out the [Text generation strategies](../generation_strategies) page.
 
 ```py

docs/source/en/tasks/summarization.md

@@ -355,7 +355,7 @@ Tokenize the text and return the `input_ids` as PyTorch tensors:
 >>> inputs = tokenizer(text, return_tensors="pt").input_ids
 ```
 
-Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API.
+Use the [`~generation.GenerationMixin.generate`] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API.
 
 ```py
 >>> from transformers import AutoModelForSeq2SeqLM

docs/source/en/tasks/translation.md

@@ -364,7 +364,7 @@ Tokenize the text and return the `input_ids` as PyTorch tensors:
 >>> inputs = tokenizer(text, return_tensors="pt").input_ids
 ```
 
-Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API.
+Use the [`~generation.GenerationMixin.generate`] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API.
 
 ```py
 >>> from transformers import AutoModelForSeq2SeqLM

docs/source/ja/generation_strategies.md

@@ -18,7 +18,7 @@ rendered properly in your Markdown viewer.
 
 テキスト生成は、オープンエンドのテキスト生成、要約、翻訳など、多くの自然言語処理タスクに不可欠です。また、テキストを出力とするさまざまな混在モダリティアプリケーションにも影響を与えており、例えば音声からテキストへの変換や画像からテキストへの変換などがあります。テキストを生成できるいくつかのモデルには、GPT2、XLNet、OpenAI GPT、CTRL、TransformerXL、XLM、Bart、T5、GIT、Whisperが含まれます。
 
-[`~transformers.generation_utils.GenerationMixin.generate`] メソッドを使用して、異なるタスクのテキスト出力を生成するいくつかの例をご紹介します：
+[`~generation.GenerationMixin.generate`] メソッドを使用して、異なるタスクのテキスト出力を生成するいくつかの例をご紹介します：
 * [テキスト要約](./tasks/summarization#inference)
 * [画像のキャプション](./model_doc/git#transformers.GitForCausalLM.forward.example)
 * [音声の転記](./model_doc/whisper#transformers.WhisperForConditionalGeneration.forward.example)

docs/source/ja/tasks/language_modeling.md

@@ -388,7 +388,7 @@ TensorFlow でモデルを微調整するには、オプティマイザー関数
 >>> inputs = tokenizer(prompt, return_tensors="pt").input_ids
 ```
 
-[`~transformers.generation_utils.GenerationMixin.generate`] メソッドを使用してテキストを生成します。
+[`~generation.GenerationMixin.generate`] メソッドを使用してテキストを生成します。
 さまざまなテキスト生成戦略と生成を制御するためのパラメーターの詳細については、[テキスト生成戦略](../generation_strategies) ページを参照してください。
 
 ```py

docs/source/ja/tasks/summarization.md

@@ -358,7 +358,7 @@ Tokenize the text and return the `input_ids` as PyTorch tensors:
 >>> inputs = tokenizer(text, return_tensors="pt").input_ids
 ```
 
-[`~transformers.generation_utils.GenerationMixin.generate`] メソッドを使用して要約を作成します。さまざまなテキスト生成戦略と生成を制御するためのパラメーターの詳細については、[Text Generation](../main_classes/text_generation) API を確認してください。
+[`~generation.GenerationMixin.generate`] メソッドを使用して要約を作成します。さまざまなテキスト生成戦略と生成を制御するためのパラメーターの詳細については、[Text Generation](../main_classes/text_generation) API を確認してください。
 
 ```py
 >>> from transformers import AutoModelForSeq2SeqLM

docs/source/ja/tasks/translation.md

@@ -366,7 +366,7 @@ TensorFlow でモデルを微調整するには、オプティマイザー関数
 >>> inputs = tokenizer(text, return_tensors="pt").input_ids
 ```
 
-[`~transformers.generation_utils.GenerationMixin.generate`] メソッドを使用して翻訳を作成します。さまざまなテキスト生成戦略と生成を制御するためのパラメーターの詳細については、[Text Generation](../main_classes/text_generation) API を確認してください。
+[`~generation.GenerationMixin.generate`] メソッドを使用して翻訳を作成します。さまざまなテキスト生成戦略と生成を制御するためのパラメーターの詳細については、[Text Generation](../main_classes/text_generation) API を確認してください。
 
 
 ```py

docs/source/ko/generation_strategies.md

@@ -19,7 +19,7 @@ rendered properly in your Markdown viewer.
 텍스트 생성은 개방형 텍스트 작성, 요약, 번역 등 다양한 자연어 처리(NLP) 작업에 필수적입니다. 이는 또한 음성-텍스트 변환, 시각-텍스트 변환과 같이 텍스트를 출력으로 하는 여러 혼합 모달리티 응용 프로그램에서도 중요한 역할을 합니다. 텍스트 생성을 가능하게 하는 몇몇 모델로는 GPT2, XLNet, OpenAI GPT, CTRL, TransformerXL, XLM, Bart, T5, GIT, Whisper 등이 있습니다.
 
 
-[`~transformers.generation_utils.GenerationMixin.generate`] 메서드를 활용하여 다음과 같은 다양한 작업들에 대해 텍스트 결과물을 생성하는 몇 가지 예시를 살펴보세요:
+[`~generation.GenerationMixin.generate`] 메서드를 활용하여 다음과 같은 다양한 작업들에 대해 텍스트 결과물을 생성하는 몇 가지 예시를 살펴보세요:
 * [텍스트 요약](./tasks/summarization#inference)
 * [이미지 캡셔닝](./model_doc/git#transformers.GitForCausalLM.forward.example)
 * [오디오 전사](./model_doc/whisper#transformers.WhisperForConditionalGeneration.forward.example)

docs/source/ko/tasks/language_modeling.md

@@ -366,7 +366,7 @@ TensorFlow에서 모델을 미세 조정하려면, 먼저 옵티마이저 함수
 >>> inputs = tokenizer(prompt, return_tensors="pt").input_ids
 ```
 
-[`~transformers.generation_utils.GenerationMixin.generate`] 메소드를 사용하여 텍스트를 생성하세요. 생성을 제어하는 다양한 텍스트 생성 전략과 매개변수에 대한 자세한 내용은 [텍스트 생성 전략](../generation_strategies) 페이지를 확인하세요.
+[`~generation.GenerationMixin.generate`] 메소드를 사용하여 텍스트를 생성하세요. 생성을 제어하는 다양한 텍스트 생성 전략과 매개변수에 대한 자세한 내용은 [텍스트 생성 전략](../generation_strategies) 페이지를 확인하세요.
 
 ```py
 >>> from transformers import AutoModelForCausalLM

docs/source/ko/tasks/summarization.md

@@ -366,7 +366,7 @@ TensorFlow에서 모델을 파인튜닝하려면, 먼저 옵티마이저, 학습
 >>> inputs = tokenizer(text, return_tensors="pt").input_ids
 ```
 
-요약문을 생성하려면 [`~transformers.generation_utils.GenerationMixin.generate`] 메소드를 사용하세요. 
+요약문을 생성하려면 [`~generation.GenerationMixin.generate`] 메소드를 사용하세요.
 텍스트 생성에 대한 다양한 전략과 생성을 제어하기 위한 매개변수에 대한 자세한 내용은 [텍스트 생성](../main_classes/text_generation) API를 참조하세요.
 
 ```py

docs/source/ko/tasks/translation.md

@@ -362,7 +362,7 @@ TensorFlow에서 모델을 파인튜닝하려면 우선 optimizer 함수, 학습
 >>> inputs = tokenizer(text, return_tensors="pt").input_ids
 ```
 
-[`~transformers.generation_utils.GenerationMixin.generate`] 메서드로 번역을 생성하세요. 다양한 텍스트 생성 전략 및 생성을 제어하기 위한 매개변수에 대한 자세한 내용은 [Text Generation](../main_classes/text_generation) API를 살펴보시기 바랍니다.
+[`~generation.GenerationMixin.generate`] 메서드로 번역을 생성하세요. 다양한 텍스트 생성 전략 및 생성을 제어하기 위한 매개변수에 대한 자세한 내용은 [Text Generation](../main_classes/text_generation) API를 살펴보시기 바랍니다.
 
 ```py
 >>> from transformers import AutoModelForSeq2SeqLM

src/transformers/generation/configuration_utils.py

@@ -218,8 +218,7 @@ class GenerationConfig(PushToHubMixin):
             Switch to sequential beam search and sequential topk for contrastive search to reduce peak memory.
             Used with beam search and contrastive search.
 
-
-        > Parameters that define the output variables of `generate`
+        > Parameters that define the output variables of generate
 
         num_return_sequences(`int`, *optional*, defaults to 1):
             The number of independently computed returned sequences for each element in the batch.
@@ -256,25 +255,21 @@ class GenerationConfig(PushToHubMixin):
             `batch_size`. Indicating a list enables different start ids for each element in the batch
             (e.g. multilingual models with different target languages in one batch)
 
-
-        > Generation parameters exclusive to [assistant generation](https://arxiv.org/abs/2211.17192)
+        > Generation parameters exclusive to assistant generation
 
         num_assistant_tokens (`int`, *optional*, defaults to 5):
             Defines the number of _speculative tokens_ that shall be generated by the assistant model before being
             checked by the target model at each iteration. Higher values for `num_assistant_tokens` make the generation
             more _speculative_ : If the assistant model is performant larger speed-ups can be reached, if the assistant
             model requires lots of corrections, lower speed-ups are reached.
-
         num_assistant_tokens_schedule (`str`, *optional*, defaults to `"heuristic"`):
             Defines the schedule at which max assistant tokens shall be changed during inference.
             - `"heuristic"`: When all speculative tokens are correct, increase `num_assistant_tokens` by 2 else
               reduce by 1. `num_assistant_tokens` value is persistent over multiple generation calls with the same assistant model.
             - `"heuristic_transient"`: Same as `"heuristic"` but `num_assistant_tokens` is reset to its initial value after each generation call.
             - `"constant"`: `num_assistant_tokens` stays unchanged during generation
-
         prompt_lookup_num_tokens (`int`, *optional*, default to `None`):
             The number of tokens to be output as candidate tokens.
-
         max_matching_ngram_size (`int`, *optional*, default to `None`):
             The maximum ngram size to be considered for matching in the prompt. Default to 2 if not provided.
 
@@ -283,7 +278,6 @@ class GenerationConfig(PushToHubMixin):
         cache_implementation (`str`, *optional*, default to `None`):
             Cache class that should be used when generating.
 
-
         > Wild card
 
         generation_kwargs: