bart-base-open-instructiongen-v1
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
Model description
This model is a fine-tuned version of facebook/bart-base on the hakurei/open-instruct-v1 dataset.
- This model only generates the
instruction
for arbitrary text (it does not provideinputs
as well - look for models withw-inputs
in the name). - There was no validation split at the time of training, so no statistics here.
- Comparing the performance of this model with pszemraj/bart-base-instructiongen might give some indication of whether and how much dataset scaling is needed to produce "robust" instruction generators.
- If you notice any trends, feel free to reach out! would be happy to hear about it.
Training and evaluation data
See hakurei/open-instruct-v1
. This model was trained on the dataset "backwards", i.e. the model was given the output
column as input and trained to predict instruction
.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2.0
Training results
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.9.0
- Tokenizers 0.12.1
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