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license: bigscience-openrail-m |
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datasets: |
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- apcl/so13m |
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- apcl/jm52m |
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# Jam_sojm |
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Jam_sojm is a GPT2-like model for research in fine-grained Java analysis. It is intended for fine-grained analysis of Java source code at the level of methods, statements, and variables, as a foundation for downstream tasks like code completion, comment generation, and automated bug repair. |
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## Jam_sojm Training Details |
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- We trained the jam_sojm model using the training procedures from Daniel Grittner's [NanoGPT-LoRA](https://github.com/danielgrittner/nanoGPT-LoRA) |
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- The datasets used to train our model are our own datasets [so13m dataset](https://huggingface.co/datasets/apcl/so13m) and [jm52m dataset](https://huggingface.co/datasets/apcl/jm52m). |
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- First we train the model on [so13m training set](https://huggingface.co/datasets/apcl/so13m/blob/main/train.bin) for 1 epoch, roughly 300,000 training iterations. |
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- We reset the learning rate and weight decay, then train it again on the [jm52mm training set](https://huggingface.co/datasets/apcl/jm52m/blob/main/train.bin) for 1 more epoch, roughly 300,000 more training iterations for a total of 600,000 iterations. |
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- Our [GitHub repo](https://github.com/apcl-research/jam/blob/main) contains the code for re-training using the [raw data](https://huggingface.co/datasets/apcl/so13m/blob/main/so13m.pkl). |
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| Hyperparameter | Description | Value | |
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|e | embedding dimensions | 1024 | |
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|L | number of layers | 24 | |
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|h | attention heads | 16 | |
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|c | block size / context length | 256 | |
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|b | batch size | 4 | |
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|a | accumulation steps | 32 | |
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|d | dropout | 0.20 | |
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|r | learning rate | 3e-5 | |
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|y | weight decay | 1e-1 | |
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We train our models using a single NVidia A5000 GPUs. |
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## Jam Projects |
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Current projects using the jam_sojm pre-trained model can be found at our Github repository: |
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https://github.com/apcl-research/jam |
