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A newer version of the Gradio SDK is available:
4.42.0
Based on https://github.com/tloen/alpaca-lora
Instructions
- Download a LoRA, for instance:
python download-model.py tloen/alpaca-lora-7b
- Load the LoRA. 16-bit, 8-bit, and CPU modes work:
python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b
python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --load-in-8bit
python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --cpu
For using LoRAs in 4-bit mode, follow these special instructions.
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
You can train your own LoRAs from the Training
tab. See Training LoRAs for details.