text-generation-webui/docs/Using-LoRAs.md

Based on https://github.com/tloen/alpaca-lora

Instructions

1. Download a LoRA, for instance:

python download-model.py tloen/alpaca-lora-7b

2. Load the LoRA. 16-bit, --load-in-8bit, --load-in-4bit, and CPU modes work:

python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b
python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --load-in-8bit
python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --load-in-4bit
python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --cpu

• For using LoRAs with GPTQ quantized models, follow these special instructions.

• Instead of using the --lora command-line flag, you can also select the LoRA in the "Parameters" tab of the interface.

Prompt

For the Alpaca LoRA in particular, the prompt must be formatted like this:

Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Write a Python script that generates text using the transformers library.
### Response:

Sample output:

Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Write a Python script that generates text using the transformers library.
### Response:

import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForCausalLM.from_pretrained("bert-base-uncased")
texts = ["Hello world", "How are you"]
for sentence in texts:
sentence = tokenizer(sentence)
print(f"Generated {len(sentence)} tokens from '{sentence}'")
output = model(sentences=sentence).predict()
print(f"Predicted {len(output)} tokens for '{sentence}':\n{output}")

Training a LoRA

You can train your own LoRAs from the Training tab. See Training LoRAs for details.