langchain/docs/extras/integrations/providers/bittensor.mdx
Aashish Saini f6f0b0f975
Fixed typo in bittensor.mdx (#10160)
Fixed Typo in bittenaor.mdx

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# NIBittensor
This page covers how to use the BittensorLLM inference runtime within LangChain.
It is broken into two parts: installation and setup, and then examples of NIBittensorLLM usage.
## Installation and Setup
- Install the Python package with `pip install langchain`
## Wrappers
### LLM
There exists a NIBittensor LLM wrapper, which you can access with:
```python
from langchain.llms import NIBittensorLLM
```
It provides a unified interface for all models:
```python
llm = NIBittensorLLM(system_prompt="Your task is to provide concise and accurate response based on user prompt")
print(llm('Write a fibonacci function in python with golder ratio'))
```
Multiple responses from top miners can be accessible using the `top_responses` parameter:
```python
multi_response_llm = NIBittensorLLM(top_responses=10)
multi_resp = multi_response_llm("What is Neural Network Feeding Mechanism?")
json_multi_resp = json.loads(multi_resp)
print(json_multi_resp)
```