Instructions to use lxaw/dora_model-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lxaw/dora_model-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("data4elm/Llama-400M-12L") model = PeftModel.from_pretrained(base_model, "lxaw/dora_model-adapter") - Notebooks
- Google Colab
- Kaggle
dora_model-adapter
Adapter only for DoRA-finetuned Llama-400M model
Adapter Details
This is the DoRA adapter for lxaw/dora_model.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load the base model first
base_model = AutoModelForCausalLM.from_pretrained("YongganFu/Llama-400M-12L")
# Load the DoRA adapter
model = PeftModel.from_pretrained(base_model, "lxaw/dora_model-adapter")
# Load the tokenizer from the base model
tokenizer = AutoTokenizer.from_pretrained("YongganFu/Llama-400M-12L")
# Example usage
input_text = "What is the capital of France?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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