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57 lines
1.9 KiB
Markdown
57 lines
1.9 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, `--load-in-8bit`, `--load-in-4bit`, and CPU modes work:
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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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* For using LoRAs with GPTQ quantized models, follow [these special instructions](GPTQ-models-(4-bit-mode).md#using-loras-with-gptq-for-llama).
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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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