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
```

* For using LoRAs in 4-bit mode, follow [these special instructions](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode).

* 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

The Training tab in the interface can be used to train a LoRA. The parameters are self-documenting and good defaults are included.

This was contributed by [mcmonkey4eva](https://github.com/mcmonkey4eva) in PR [#570](https://github.com/oobabooga/text-generation-webui/pull/570).


#### Using the original alpaca-lora code

Kept here for reference. The Training tab has much more features than this method.

```
conda activate textgen
git clone https://github.com/tloen/alpaca-lora
```

Edit those two lines in `alpaca-lora/finetune.py` to use your existing model folder instead of downloading everything from decapoda:

```
model = LlamaForCausalLM.from_pretrained(
    "models/llama-7b",
    load_in_8bit=True,
    device_map="auto",
)
tokenizer = LlamaTokenizer.from_pretrained(
    "models/llama-7b", add_eos_token=True
)
```

Run the script with:

```
python finetune.py
```

It just works. It runs at 22.32s/it, with 1170 iterations in total, so about 7 hours and a half for training a LoRA. RTX 3090, 18153MiB VRAM used, drawing maximum power (350W, room heater mode).