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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
+```
+
+* For using LoRAs in 4-bit mode, follow these special instructions: https://github.com/oobabooga/text-generation-webui/wiki/GPTQ-models-(4-bit-mode)#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).