A LLM operator generates answer given prompt in messages using a large language model or service.
This operator uses a pretrained [Dolly](https://github.com/databrickslabs/dolly) to generate response.
It will download model from [HuggingFace Models](https://huggingface.co/models).
<br/>
## Code Example
Use the default model to continue the conversation from given messages.
*Write a pipeline with explicit inputs/outputs name specifications:*
```python
from towhee import pipe, ops
p = (
pipe.input('messages')
.map('messages', 'answer', ops.LLM.Dolly())
.output('messages', 'answer')
)
messages=[
{'question': 'Who won the world series in 2020?', 'answer': 'The Los Angeles Dodgers won the World Series in 2020.'},
{'question': 'Where was it played?'}
]
answer = p(messages)
```
<br/>
## Factory Constructor
Create the operator via the following factory method:
***LLM.Dolly(model_name: str)***
**Parameters:**
***model_name***: *str*
The model name in string, defaults to 'databricks/dolly-v2-12b'. Supported model names:
- databricks/dolly-v2-12b
- databricks/dolly-v2-7b
- databricks/dolly-v2-3b
- databricks/dolly-v1-6b
***\*\*kwargs***
Other Dolly model parameters such as device_map.
<br/>
## Interface
The operator takes a piece of text in string as input.
It returns answer in json.
***\_\_call\_\_(txt)***
**Parameters:**
***messages***: *list*
A list of messages to set up chat.
Must be a list of dictionaries with key value from "system", "question", "answer". For example, [{"question": "a past question?", "answer": "a past answer."}, {"question": "current question?"}]
