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

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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, 8-bit, and CPU modes work:
```
python server.py --model llama-7b-hf --lora alpaca-lora-7b
python server.py --model llama-7b-hf --lora alpaca-lora-7b --load-in-8bit
python server.py --model llama-7b-hf --lora alpaca-lora-7b --cpu
```
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* For using LoRAs in 4-bit mode, follow [these special instructions](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode).
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* 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
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You can train your own LoRAs from the `Training` tab. See [Training LoRAs](Training-LoRAs.md) for details.