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# RWKV-4

This page covers how to use the `RWKV-4` wrapper within LangChain. It is broken into two parts: installation and setup, and then usage with an example.

## Installation and Setup

- Install the Python package with `pip install rwkv`

- Install the tokenizer Python package with `pip install tokenizer`

- Download a RWKV model and place it in your desired directory

- Download the tokens file

## Usage

### RWKV

To use the RWKV wrapper, you need to provide the path to the pre-trained model file and the tokenizer’s configuration.

    from langchain.llms import RWKV

    # Test the model

    ```python

    def generate_prompt(instruction, input=None):
        if input:
            return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

    # Instruction:
    {instruction}

    # Input:
    {input}

    # Response:
    """
        else:
            return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.

    # Instruction:
    {instruction}

    # Response:
    """

    model = RWKV(model="./models/RWKV-4-Raven-3B-v7-Eng-20230404-ctx4096.pth", strategy="cpu fp32", tokens_path="./rwkv/20B_tokenizer.json")
    response = model(generate_prompt("Once upon a time, "))

## Model File

You can find links to model file downloads at the RWKV-4-Raven repository.

### Rwkv-4 models -> recommended VRAM

    RWKV VRAM
    Model | 8bit | bf16/fp16 | fp32
    14B   | 16GB | 28GB      | >50GB
    7B    | 8GB  | 14GB      | 28GB
    3B    | 2.8GB| 6GB       | 12GB
    1b5   | 1.3GB| 3GB       | 6GB

See the rwkv pip page for more information about strategies, including streaming and cuda support.