Instead of generating questions from text, generate instructions for LLMs!
- Check out a basic demo on Spaces
- An example of how to use instructiongen models in a CLI script can be found here
- You can find other models fine-tuned for instruction generation by searching for the instructiongen tag.
Hypothesis: Apply text-to-text models to unlabeled domain-specific text to generate appropriate LLM instructions. Consequently, this may enable domain adaptation of instruction-tuned LLMs, making them more versatile for specific domains.
This model is a fine-tuned version of the facebook/bart-base model, fine-tuned using the
It achieves the following results on the evaluation set:
- Loss: 1.0034
- Rouge1: 61.7209
- Rouge2: 45.0116
- Rougel: 59.8188
- Rougelsum: 59.8931
- Gen Len: 14.3179
This is just a base model/example. There is likely to be even better performance with larger models (click here to see other checkpoints)
Additionally, this was trained on a dataset of only instructions+outputs, with the
inputs filtered out. This means that text of 1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo will not get you "Rank the following ice cream flavors: oreo, mint chip, chocolate chip, cookies and cream".
See the linked dataset
pszemraj/fleece2instructions - it is a filtered/formatted version of
tatsu-lab/alpaca to generate instructions for arbitrary text.
- Some of the API examples are intentionally weird to demonstrate the generalizability of the model.
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- 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: 2.0
|Training Loss||Epoch||Step||Validation Loss||Rouge1||Rouge2||Rougel||Rougelsum||Gen Len|
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