Update README.md

README.md

@@ -11,4 +11,21 @@ tags:
 We fine-tuned Llama2-7b-chat using LoRA. We used a batch size of 1 and a chunk size of 2048. Training involved the use of the AdamW optimizer with a learning rate of 2e-5 and gradient accumulation steps set at 8. A single training epoch was performed, along with a warm-up step of 0.03 and a weight decay rate of 0.001. The learning rate was controlled using a cosine learning rate scheduler. LoRA adapters, characterized by a rank of 8, an alpha value of 32, and a dropout rate of 0.1, were applied after all self-attention blocks and fully-connected layers. This results in total 17,891,328 trainable parameters, roughly 0.26% of the entire parameters of the base model. To optimize training performance, bf16 mixed precision training and data parallelism were employed. We used 4 Nvidia A100 (80GB) GPUs hosted on the Microsoft Azure platform. An epoch of training takes roughly 42 GPU hours.
 
 ## Training data:
-Please refer to Dataset card: https://huggingface.co/datasets/BrainGPT/train_valid_split_pmc_neuroscience_2002-2022_filtered_subset
+Please refer to Dataset card: https://huggingface.co/datasets/BrainGPT/train_valid_split_pmc_neuroscience_2002-2022_filtered_subset
+
+## Load and use model:
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+
+config = PeftConfig.from_pretrained("BrainGPT/BrainGPT-7B-v0.1")
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
+model = PeftModel.from_pretrained(model, "BrainGPT/BrainGPT-7B-v0.1")
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+```