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README.md

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 # Model Details
 
-The TinyCodeLM family of tiny language models (LMs) is a collection of fully open-source pretrained and instruction tuned generative code models in 150M and 400M sizes. These models are pretrained on a mixture of open-source web text and Python code. The instruction tuned TinyCodeLM models are optimized for Python code synthesis, and are trained on [synthetic edit sequence data generated with the LintSeq algorithm](https://arxiv.org/abs/2410.02749).
+The TinyCodeLM family of tiny language models (LMs) is a collection of fully open-source pretrained and instruction tuned generative code models in 150M and 400M sizes. These models are pretrained on a mixture of open-source web text and Python code. The instruction tuned TinyCodeLM models are optimized for Python code synthesis, and are trained on [synthetic edit sequence data generated with the LintSeq algorithm](https://lintseq.github.io/).
 
 Despite being trained on only 72 billion tokens of text, the models outperform many of the available open source Python code synthesis models on HumanEval and MBPP. The TinyCodeLM-LintSeqInstruct models are state-of-the-art on Python synthesis for their size.