Update README.md

README.md

@@ -1,3 +1,39 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- FreedomIntelligence/Huatuo26M-Lite
+- CocoNutZENG/NeuroQABenchmark
+language:
+- zh
+- en
+metrics:
+- accuracy
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+tags:
+- medical
+---
+## Introduction
+Train LLM to be neuroscientist. It expected to work in Chinese and Engliah environment.
+
+## Data
+1. [FreedomIntelligence/Huatuo26M-Lite](https://huggingface.co/datasets/FreedomIntelligence/Huatuo26M-Lite). We select neuroscience(神经科学) label as train data.
+2. [CocoNutZENG/NeuroQABenchmark](https://huggingface.co/datasets/CocoNutZENG/NeuroQABenchmark)
+
+
+## Train Detail
+We fine-tuned the Qwen2.5 model using supervised fine-tuning (SFT) with LoRA for efficient parameter optimization. The LoRA configuration employed a rank of 8 (R=8) to
+balance adaptation quality with computational efficiency. Training was conducted for 1 epoch (approximately 1 hour duration) using two NVIDIA A40 GPUs with DeepSpeed’s Stage 2 optimization
+for memory efficiency. We adopted the Adam optimizer with an initial learning rate of 5e-5 and a
+cosine learning rate scheduler for smooth decay. This configuration achieved effective model adaptation while maintaining computational tractability on our hardware setup. Our model’s loss drop as
+expected, see figure below for loss detail.
+[![image.png](https://i.postimg.cc/d30KMJCQ/image.png)](https://postimg.cc/62FPnJzF)
+
+## Evalution
+| Model          | Acc   |
+|----------------|-------|
+| Qwen2.5-3b     | 0.788 |
+| Qwen2.5-7b     | 0.820 |
+| +Huatu0Lite    | 0.832 |
+| +Full data     | 0.848 |
+