Add link to Github and improve description

README.md

@@ -1,11 +1,11 @@
 ---
-license: apache-2.0
 datasets:
 - togethercomputer/RedPajama-Data-1T
 language:
 - en
-pipeline_tag: text-generation
 library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
 ---
 
 ## PDS-1.7B
@@ -14,7 +14,8 @@ library_name: transformers
 
 **PDS-1.7B** is a 1.7B model with [Mistral](https://arxiv.org/abs/2310.06825) achitecture pre-trained from scratch on the data selected from the CC split of [Redpajama](https://github.com/togethercomputer/RedPajama-Data), using the PDS framework.
 
-The PDS framework is based on the [Pontryagin's maximum principle](https://en.wikipedia.org/wiki/Pontryagin%27s_maximum_principle#:~:text=Pontryagin's%20maximum%20principle%20is%20used,the%20state%20or%20input%20controls.) for optimal pre-training data selection, which not only enjoy strong theoretical support but is also scalable for training large language models. 
+This work investigates the selection of high-quality pre-training data from massive corpora to enhance LMs' capabilities for downstream usage.
+We formulate data selection as a generalized Optimal Control problem, which can be solved theoretically by Pontryagin's Maximum Principle (PMP), yielding a set of necessary conditions that characterize the relationship between optimal data selection and LM training dynamics. Based on these theoretical results, we introduce PMP-based Data Selection (PDS), a framework that approximates optimal data selection by solving the PMP conditions.
 
 Please refer to our [paper](https://arxiv.org/abs/2410.07064) for more details.
 
@@ -42,6 +43,21 @@ PDS-selected data improves the performance of language models pre-trained from s
 
 [Conventional Pre-training](https://huggingface.co/Data-Selection/BSL-1.7B)
 
+### Sample Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Data-Selection/PDS-1.7B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+inputs = tokenizer("Hello, my name is", return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
 ### Citation
 
 ```bibtex
@@ -51,4 +67,4 @@ PDS-selected data improves the performance of language models pre-trained from s
   journal={arXiv preprint arXiv:2410.07064},
   year={2024}
 }
-```
+```