| language: | |
| - en | |
| tags: | |
| - llama | |
| - peft | |
| - dora | |
| - lora | |
| license: apache-2.0 | |
| base_model: YongganFu/Llama-400M-12L | |
| # dora_model | |
| DoRA-finetuned Llama-400M model | |
| ## Model Details | |
| This model is a DoRA-finetuned version of [YongganFu/Llama-400M-12L](https://huggingface.co/YongganFu/Llama-400M-12L). | |
| The standalone adapter is available at [lxaw/dora_model-adapter](https://huggingface.co/lxaw/dora_model-adapter). | |
| ## Usage | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| # Option 1: Load the complete model directly | |
| model = AutoModelForCausalLM.from_pretrained("lxaw/dora_model") | |
| tokenizer = AutoTokenizer.from_pretrained("lxaw/dora_model") | |
| # Option 2: Load just the adapter with the base model | |
| base_model = AutoModelForCausalLM.from_pretrained("YongganFu/Llama-400M-12L") | |
| tokenizer = AutoTokenizer.from_pretrained("YongganFu/Llama-400M-12L") | |
| model = PeftModel.from_pretrained(base_model, "lxaw/dora_model-adapter") | |
| # 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)) | |
| ``` |