Update README.md

README.md

@@ -5,4 +5,40 @@ datasets:
 base_model:
 - deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
 library_name: peft
----
+language:
+- en
+- zh
+pipeline_tag: question-answering
+tags:
+- reasoning
+---
+
+## Introduction
+
+Tina (Tiny Reasoning Models via LoRA) models are all fine-tuned adapters on the base model [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B). 
+This LoRA adapter in this repo is fine-tuned with the dataset [knoveleng/open-s1](https://huggingface.co/datasets/knoveleng/open-s1).
+Please refer to our paper [Tina: Tiny Reasoning Models via LoRA](https://arxiv.org/abs/2504.15777) for more training details.
+
+
+## Example Usage
+
+The Tina model is meant to be used in combination with the base model as a standard adapter. Particularly, we release all checkpoints we have for each Tina model and one could select different checkpoint to use by specifying the `subfolder`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained(
+  "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
+  device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+  "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+)
+
+model = PeftModel.from_pretrained(
+  base_model,
+  "Tina-Yi/R1-Distill-Qwen-1.5B-Open-RS1",
+  subfolder="checkpoint-800" # checkpoint 800 is the best
+)
+```