  
  An LLM component is implemented through the `LLMWrapper` class. It is accessible
  through a generic `llm`
  [component factory](https://spacy.io/usage/processing-pipelines#custom-components-factories)
  as well as through task-specific component factories:
  
  - `llm_ner`
  - `llm_spancat`
  - `llm_rel`
  - `llm_textcat`
  - `llm_sentiment`
  - `llm_summarization`
  
  ### LLMWrapper.\_\_init\_\_ {id="init",tag="method"}
  
  > #### Example
  >
 
</s> remove See the "HF directory" for more details on each of the models.

 </s> add  </s> remove > # Construction via add_pipe with default GPT3.5 model and NER task
 </s> add > # Construction via add_pipe with the default GPT 3.5 model and an explicitly defined task </s> remove > # Construction via add_pipe with task-specific factory and default GPT3.5 model
> parser = nlp.add_pipe("llm-ner", config=config)
 </s> add > # Construction via add_pipe with a task-specific factory and default GPT3.5 model
> llm = nlp.add_pipe("llm-ner")
as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`,
`llm_textcat`, `llm_sentiment` and `llm_summarization`.
  
  An LLM component is implemented through the `LLMWrapper` class. It is accessible
  through a generic `llm`
  [component factory](https://spacy.io/usage/processing-pipelines#custom-components-factories)
as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`,
`llm_textcat`, `llm_sentiment` and `llm_summarization`.
as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`,
`llm_textcat`, `llm_sentiment` and `llm_summarization`.
as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`,
`llm_textcat`, `llm_sentiment` and `llm_summarization`.
as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`,
`llm_textcat`, `llm_sentiment` and `llm_summarization`.
as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`,
`llm_textcat`, `llm_sentiment` and `llm_summarization`.
as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`,
`llm_textcat`, `llm_sentiment` and `llm_summarization`.
as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`,
`llm_textcat`, `llm_sentiment` and `llm_summarization`.
as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`,
`llm_textcat`, `llm_sentiment` and `llm_summarization`.
  
  ### LLMWrapper.\_\_init\_\_ {id="init",tag="method"}
  
  > #### Example
  >
keep keep keep keep replace replace replace replace replace replace replace replace keep keep keep keep keep
 Few spacy-llm doc fixes (#12969)

* fix construction example

* shorten task-specific factory list

* small edits to HF models

* small edit to API models

* typo

* fix space

Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>

---------

Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com> 
website/docs/api/large-language-models.mdx