langchain/docs/versioned_docs/version-0.2.x/integrations/providers/rwkv.mdx

# 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](https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main) and place it in your desired directory
- Download the [tokens file](https://raw.githubusercontent.com/BlinkDL/ChatRWKV/main/20B_tokenizer.json)

## Usage

### RWKV

To use the RWKV wrapper, you need to provide the path to the pre-trained model file and the tokenizer's configuration.
```python
from langchain_community.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](https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main) 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](https://pypi.org/project/rwkv/) page for more information about strategies, including streaming and cuda support.