mirror of
https://github.com/hwchase17/langchain
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66 lines
1.9 KiB
Plaintext
66 lines
1.9 KiB
Plaintext
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# RWKV-4
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This page covers how to use the `RWKV-4` wrapper within LangChain.
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It is broken into two parts: installation and setup, and then usage with an example.
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## Installation and Setup
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- Install the Python package with `pip install rwkv`
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- Install the tokenizer Python package with `pip install tokenizer`
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- Download a [RWKV model](https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main) and place it in your desired directory
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- Download the [tokens file](https://raw.githubusercontent.com/BlinkDL/ChatRWKV/main/20B_tokenizer.json)
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## Usage
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### RWKV
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To use the RWKV wrapper, you need to provide the path to the pre-trained model file and the tokenizer's configuration.
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```python
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from langchain.llms import RWKV
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# Test the model
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```python
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def generate_prompt(instruction, input=None):
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if input:
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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.
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# Instruction:
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{instruction}
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# Input:
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{input}
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# Response:
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"""
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else:
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return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
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# Instruction:
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{instruction}
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# Response:
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"""
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model = RWKV(model="./models/RWKV-4-Raven-3B-v7-Eng-20230404-ctx4096.pth", strategy="cpu fp32", tokens_path="./rwkv/20B_tokenizer.json")
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response = model(generate_prompt("Once upon a time, "))
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```
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## Model File
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You can find links to model file downloads at the [RWKV-4-Raven](https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main) repository.
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### Rwkv-4 models -> recommended VRAM
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```
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RWKV VRAM
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Model | 8bit | bf16/fp16 | fp32
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14B | 16GB | 28GB | >50GB
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7B | 8GB | 14GB | 28GB
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3B | 2.8GB| 6GB | 12GB
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1b5 | 1.3GB| 3GB | 6GB
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```
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See the [rwkv pip](https://pypi.org/project/rwkv/) page for more information about strategies, including streaming and cuda support.
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