Update README.md

README.md

@@ -13,9 +13,10 @@ This is an unofficial implementation of "[AlpaGasus: Training a better Alpaca wi
 - **License**: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/))
 
 
-### Training Dataset
+### Training dataset
 
 "StudentLLM/Alpagasus-2-13b-QLoRA-merged" used [gpt4life](https://github.com/gpt4life/alpagasus)'s gpt-3.5-turbo filtered dataset, 'alpaca_t45.json'.
+
 Configuration of the dataset is as follows:
 
 ```
@@ -58,9 +59,34 @@ Our model was finetuned using QLoRA on single A100 80GB GPU. Training details ar
 | TruthfulQA            | 38.53 |
 
 ### LLM Evaluation
+We tried to follow the evaluation metric introduced by the AlpaGasus paper. During the process, we consulted the code by [gpt4life](https://github.com/gpt4life/alpagasus). We used OpenAI's gpt-3.5-turbo as the evaluator model, and Alpaca2-LoRA-13B(it doesn't exist now...) as the comparison model. For more detailed information, please refer to our Github [repo](https://github.com/gauss5930/AlpaGasus2-QLoRA).
 
-### Fine-tuning Procedure
-Our mod
+The evaluation result of AlpaGasus2-QLoRA is as follows:
+![results](https://user-images.githubusercontent.com/80087878/262848860-8742bcc4-1bbc-449f-8bcf-660c08fcc10d.png)
+
+### How to use
+To use "StudentLLM/Alpagasus-2-13b-QLoRA-merged", please follow the following code! The use of the 7B model is the same!
+```
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+config = PeftConfig.from_pretrained("StudentLLM/Alpagasus-2-13B-QLoRA")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf", use_auth_token="yotu_HuggingFace_token").to(device)
+model = PeftModel.from_pretrained(model, "StudentLLM/Alpagasus-2-13B-QLoRA")
+
+tokenizer = AutoTokenizer.from_pretrained("StudentLLM/Alpagasus-2-13B-QLoRA")
+tokenizer.pad_token = tokenizer.eos_token
+
+input_data = "Please tell me 3 ways to relieve stress."   # You can enter any questions!!
+
+model_inputs = tokenizer(input_data, return_tensors='pt').to(device)
+model_output = model.generate(**model_inputs, max_length=256)
+model_output = tokenizer.decode(model_output[0], skip_special_tokens=True)
+print(model_output)
+```
 
 ### Citations
 ```bibtex