Use this text2text model to find out what LLM instructions might be able to generate an arbitary piece of code!
This model is a fine-tuned version of facebook/bart-base on the
It achieves the following results on the evaluation set:
- Loss: 1.0136
- Rouge1: 59.9513
- Rouge2: 33.9118
- Rougel: 55.7815
- Rougelsum: 56.9064
- Gen Len: 29.7146
🚨 note: as the authors elected to release the original dataset under
cc-by-nc, the license carries over to this model and cannot be used for commercial activity.
This is just a
basesize model, which does a decent job for its size, but is not perfect. For better quality instructions, check out bart-large or fine tune your own larger model on the dataset :)
Intended use: Research on domain adaptation and/or other improvements to LLMs by extending instruction:text data pairs.
Refer to the linked dataset card for
pszemraj/fleece2instructions-codealpaca or the original dataset repo.
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 3.0
|Training Loss||Epoch||Step||Validation Loss||Rouge1||Rouge2||Rougel||Rougelsum||Gen Len|
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