Update README.md

README.md

@@ -7,6 +7,7 @@ base_model:
 - upiter/TinyCodeLM-400M
 ---
 
+
 # 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).
@@ -42,7 +43,7 @@ TinyCodeLM models were pretrained from scratch on a single H100 node (four GPUs)
 | :----------- | -----------------: | -----------------: |
 |  HumanEval, pass@1   | 12.8   | 13.4  |
 |  HumanEval, pass@10 |    20.6   |  20.9  |
-|  MBPP(+), pass@1       | 13.6   |   24.4  |
+|  MBPP(+), pass@1       | 13.6   |   19.4  |
 |  MBPP(+), pass@10     |  24.4   |    29.9  |
 
 
@@ -60,4 +61,4 @@ TinyCodeLM models were pretrained from scratch on a single H100 node (four GPUs)
 ```
 
 # Safety
-This work explores data-driven mechanisms for improving the quality of language model-generated code. Our synthetic data generation method relies on open-source data and our experiments leverage open-source software and resources. It is important to acknowledge that all language models for code synthesis have the potential to be misused – whether intentionally or unintentionally – for generation of code with vulnerabilities and/or malicious behaviors. Any and all model generated code has thepotential to be harmful and must not be executed without precautions.
+This work explores data-driven mechanisms for improving the quality of language model-generated code. Our synthetic data generation method relies on open-source data and our experiments leverage open-source software and resources. It is important to acknowledge that all language models for code synthesis have the potential to be misused – whether intentionally or unintentionally – for generation of code with vulnerabilities and/or malicious behaviors. Any and all model generated code has thepotential to be harmful and must not be executed without precautions.