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# Jam-CGPT
Jam-CGPT is a GPT2-like model that follows [jam](https://huggingface.co/apcl/jam)'s pretraining procedure to pretrain models ranging from 38 million to 350 million parameters and finetuning with comments generated by GPT-3.5 and data size ranging from 170k to 2.15m.
## Jam-CGPT Training Details
- We follow [jam](https://huggingface.co/apcl/jam)'s pretraining procedure and use the same data to pretrain our 38m, 110m and 350m parameters models.
- We finetune our Jam-CGPT with the summaries generated by GPT-3.5 and 4 different dataset size [Jam-CGPT dataset](https://huggingface.co/datasets/apcl/Jam-CGPT).
- We finetune our models for 3 epochs.
- Our [GitHub repo](https://github.com/apcl-research/Jam-CGPT) contains the code for reproduction using the same [data](https://huggingface.co/datasets/apcl/Jam-CGPT).
## Jam-CGPT 38 million parameters model
| Hyperparameter | Description | Value |
| ----------- | ----------- |------------|
|e | embedding dimensions | 512 |
|L | number of layers | 4 |
|h | attention heads | 4 |
|c | block size / context length | 256 |
|b | batch size | 64 |
|a | accumulation steps | 2 |
|d | dropout | 0.20 |
|r | learning rate | 3e-5 |
|y | iterations | 1e-5 |
|iter | number of iterations after pretraing | 757,000 |
## Jam-CGPT 110 million parameters model
| Hyperparameter | Description | Value |
| ----------- | ----------- |------------|
|e | embedding dimensions | 768 |
|L | number of layers | 10|
|h | attention heads | 8 |
|c | block size / context length | 256 |
|b | batch size | 32 |
|a | accumulation steps | 4 |
|d | dropout | 0.20 |
|r | learning rate | 3e-5 |
|y | iterations | 1e-5 |
|iter | number of iterations after pretraing | 762,000 |
## Jam-CGPT 350 million parameters model
| Hyperparameter | Description | Value |
| ----------- | ----------- |------------|
|e | embedding dimensions | 1024 |
|L | number of layers | 24 |
|h | attention heads | 16 |
|c | block size / context length | 256 |
|b | batch size | 4 |
|a | accumulation steps | 32 |
|d | dropout | 0.20 |
|r | learning rate | 3e-5 |
|y | weight decay | 1e-5 |
|iter | iterations | 272,000 |
- Note that you can adjust the batch size and accumulation steps based on your GPU memory. But, the batch size * accumulation steps should be 128.
- If you finetune your models with multiple GPUs, you can turn down accumulation steps. For example, if you finetune with 2 GPUs, you will need to half the accumulation steps.
- We pretrained 38m and 110m models for 3 epochs.
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