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bd780a8223
This adds support for running RWKV with pytorch. https://github.com/hwchase17/langchain/issues/2398 This does not yet support rwkv.cpp
1.9 KiB
1.9 KiB
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.