| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - bigcode/the-stack |
| | - HuggingFaceFW/fineweb |
| | library_name: transformers |
| | --- |
| | |
| |
|
| | # 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://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. |
| |
|
| | **Model Developers** Ulyana Piterbarg, Lerrel Pinto, Rob Fergus (NYU) |
| |
|
| | **Variations** TinyCodeLM comes in two sizes (150M and 400M parameters) in pretrained and edit sequence instruction tuned variants. |
| |
|
| | **Input** Text only. |
| |
|
| | **Output** Models generate text and code. Instruction tuned models generate code via sequences of "diffs". |
| |
|
| | **Model Architecture** TinyCodeLMs are autoregressive language models with architectures that mimic the two smallest versions of GPT-2 (Radford et al., 2019), while integrating the transformer architecture changes of the OLMo models. |
| |
|
| | **Instruction Tuning Data** TinyCodeLMs are instruction tuned on paired instruction and Python edit sequence data. These edit sequences are generated with the LintSeq algorithm over a source dataset of paired instruction and Python programs drawn from the Magicoder and StarCoder2 OSS-Instruct datasets (Wei et al., 2024). |
| |
|
| | # Training Details |
| | TinyCodeLM models were pretrained from scratch on a single H100 node (four GPUs) for two epochs. Pretraining took about two days and six days, respectively. Instruction tuning was conducted on a single H100 GPU using DeepSpeed and took no more than several hours. |
| |
|
| | # Benchmarks |
| |
|
| | **Pretrained (Temperature 0)** |
| | |**Benchmark**|**TinyCodeLM 150M** |**TinyCodeLM 400M** | |
| | | :--------------------- | -----------------: | -----------------: | |
| | | HumanEval, pass@1 | 6.1 | 6.7 | |
| | | MBPP(+), pass@1 | 5.4 | 6.8 | |
| |
|
| |
|
| | **Edit Sequence / Instruction Tuned (Temperature-Tuned)** |
| | |**Benchmark** |**TinyCodeLM 150M** |**TinyCodeLM 400M** | |
| | | :----------- | -----------------: | -----------------: | |
| | | HumanEval, pass@1 | 12.8 | 13.4 | |
| | | HumanEval, pass@10 | 20.6 | 20.9 | |
| | | MBPP(+), pass@1 | 13.6 | 19.4 | |
| | | MBPP(+), pass@10 | 24.4 | 29.9 | |
| |
|
| |
|
| | # Citation |
| |
|
| | ``` |
| | @misc{piterbarg2024editseq, |
| | title={Training Language Models on Synthetic Edit Sequences Improves Code Synthesis}, |
| | author={Ulyana Piterbarg and Lerrel Pinto and Rob Fergus}, |
| | year={2024}, |
| | eprint={2410.02749}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.LG} |
| | } |
| | ``` |
| |
|
| | # 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 the potential to be harmful and must not be executed without precautions. |
