mirror of
https://github.com/oobabooga/text-generation-webui.git
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89 lines
2.7 KiB
Markdown
89 lines
2.7 KiB
Markdown
Based on https://github.com/tloen/alpaca-lora
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## Instructions
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1. Download a LoRA, 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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2. Load the LoRA. 16-bit, 8-bit, and CPU modes work:
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```
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python server.py --model llama-7b-hf --lora alpaca-lora-7b
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python server.py --model llama-7b-hf --lora alpaca-lora-7b --load-in-8bit
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python server.py --model llama-7b-hf --lora alpaca-lora-7b --cpu
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```
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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.
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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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The Training tab in the interface can be used to train a LoRA. The parameters are self-documenting and good defaults are included.
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This was contributed by [mcmonkey4eva](https://github.com/mcmonkey4eva) in PR [#570](https://github.com/oobabooga/text-generation-webui/pull/570).
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#### Using the original alpaca-lora code
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Kept here for reference. The Training tab has much more features than this method.
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```
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conda activate textgen
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git clone https://github.com/tloen/alpaca-lora
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```
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Edit those two lines in `alpaca-lora/finetune.py` to use your existing model folder instead of downloading everything from decapoda:
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```
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model = LlamaForCausalLM.from_pretrained(
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"models/llama-7b",
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load_in_8bit=True,
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device_map="auto",
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)
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tokenizer = LlamaTokenizer.from_pretrained(
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"models/llama-7b", add_eos_token=True
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)
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```
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Run the script with:
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```
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python finetune.py
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```
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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).
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