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71 lines
2.5 KiB
Markdown
71 lines
2.5 KiB
Markdown
# LoRA
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LoRA (Low-Rank Adaptation) is an extremely powerful method for customizing a base model by training only a small number of parameters. They can be attached to models at runtime.
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For instance, a 50mb LoRA can teach LLaMA an entire new language, a given writing style, or give it instruction-following or chat abilities.
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This is the current state of LoRA integration in the web UI:
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|Loader | Status |
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|--------|------|
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| Transformers | Full support in 16-bit, `--load-in-8bit`, `--load-in-4bit`, and CPU modes. |
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| ExLlama | Single LoRA support. Fast to remove the LoRA afterwards. |
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| AutoGPTQ | Single LoRA support. Removing the LoRA requires reloading the entire model.|
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## Downloading a LoRA
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The download script can be used. For instance:
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```
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python download-model.py tloen/alpaca-lora-7b
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```
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The files will be saved to `loras/tloen_alpaca-lora-7b`.
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## Using the LoRA
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The `--lora` command-line flag can be used. Examples:
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```
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python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b
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python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --load-in-8bit
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python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --load-in-4bit
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python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --cpu
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```
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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.
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## Prompt
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For the Alpaca LoRA in particular, the prompt must be formatted like this:
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```
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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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Write a Python script that generates text using the transformers library.
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### Response:
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```
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Sample output:
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```
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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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Write a Python script that generates text using the transformers library.
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### Response:
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import transformers
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
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model = AutoModelForCausalLM.from_pretrained("bert-base-uncased")
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texts = ["Hello world", "How are you"]
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for sentence in texts:
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sentence = tokenizer(sentence)
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print(f"Generated {len(sentence)} tokens from '{sentence}'")
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output = model(sentences=sentence).predict()
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print(f"Predicted {len(output)} tokens for '{sentence}':\n{output}")
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```
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## 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.
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