Based on https://github.com/tloen/alpaca-lora
python download-model.py tloen/alpaca-lora-7b
--load-in-8bit, --load-in-4bit, 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 --load-in-4bit
python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --cpu
For using LoRAs with GPTQ quantized models, 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.
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}")
You can train your own LoRAs from the Training tab. See Training LoRAs for details.