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 provide inputs as well - look for models with w-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