metadata

license: apache-2.0
datasets:
  - hakurei/open-instruct-v1
tags:
  - alpaca
  - self-instruct
  - instruction generation
  - instructiongen
  - bart
  - open-instruct
widget:
  - text: >-
      You'll need to start by choosing the right venue. Consider the type of
      atmosphere and the size of the area that will be suitable for the number
      of guests you plan to invite. Choose the right decorations based on your
      brother's interests, such as balloons in his favorite colors, banners, and
      streamers. Next, decide on the food and drinks, making sure they are tasty
      and appropriate for the occasion. Then decide on the other games, music,
      and entertainment that will make the party memorable. Finally, involve
      your brother's friends and family to help create the perfect surprise.
    example_title: birthday party
  - text: 1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo
    example_title: ice cream
  - text: >-
      Start by selecting a scale model of a building that fits the theme. Use a
      hobby knife and glue to cut and assemble the model into a ruined or
      abandoned version of itself, adding details like broken windows and
      graffiti. Create a base for the diorama using foam, plaster, or other
      materials, and paint it to resemble a ruined street or sidewalk. Add
      miniature vehicles, debris, and figures to complete the scene, and use
      weathering techniques like dry brushing and rust washes to add realism.
      Display the diorama in a shadow box or other protective case to showcase
      your work.
    example_title: Miniature diorama creation
  - text: >-
      Start by selecting clothing that is futuristic and edgy, such as leather
      jackets, neon-colored accessories, and tech-inspired patterns. Add
      accessories like goggles, cybernetic implants, and LED lights to enhance
      the cyberpunk vibe. Use makeup and body paint to create a futuristic look,
      such as metallic skin or neon makeup. Consider adding functional elements
      to your costume, such as a built-in backpack or hidden pockets for your
      tech gadgets. Finally, practice your confident walk and embrace your inner
      cyberpunk for a memorable and immersive costume experience.
    example_title: Cyberpunk costume design
  - text: >-
      Start by creating a base terrain with mountains, valleys, and other
      natural features. Use fractal noise and displacement mapping to add
      texture and detail to the terrain, and experiment with different materials
      like rock, grass, and water. Add surreal elements like floating islands,
      giant mushrooms, or impossible geometry to create a dreamlike atmosphere.
      Use lighting and color grading to enhance the mood and tone of the scene,
      and render the final image at a high resolution for maximum impact. Share
      your surreal landscape with the world and inspire others to explore the
      possibilities of 3D art.
    example_title: Surreal 3D landscape creation
  - text: >-
      Start by setting a realistic goal and creating a training plan. Build up
      your mileage gradually over time, and incorporate cross-training and
      strength exercises to prevent injury and improve endurance. Be sure to
      stay hydrated and properly fuel your body with nutritious foods. Listen to
      your body and adjust your training as needed to avoid overexertion or
      burnout. Finally, taper your training in the weeks leading up to the race
      to give your body time to rest and recover before the big day.
    example_title: Marathon training
  - text: >-
      What the hell did you just say about me, you little bug? I graduated top
      of my class in https://huggingface.co/spaces/safetensors/convert, and I've
      been involved in numerous secret tasks on PyTorch, and I have over 300
      confirmed PRs. I am trained in code optimization and I'm the top converter
      in the entire Hugging Face forces. You are nothing to me but just another
      target. I will convert your code with precision the likes of which has
      never been seen before on this Earth, mark my freaking words. 

      You think you can get away with saying your code is safe over the
      Internet? Think again, bug. As we speak I am contacting my secret network
      of data scientists across the GitHub and your IP is being traced right now
      so you better prepare for the storm, maggot. The storm that wipes out the
      pathetic little thing you call your code. You’re freaking doomed, kid. I
      can be anywhere, anytime, and I can convert your code in over seven
      hundred ways, and that’s just with my bare hands.

      Not only am I extensively trained in unarmed conversion, but I have access
      to the entire arsenal of the Hugging Face and I will use it to its full
      extent to wipe your miserable code off the face of the continent, you
      little bug. If only you could have known what unholy retribution your
      little "clever" comment was about to bring down upon you, maybe you would
      have held your freaking tongue. 

      But you couldn’t, you didn’t, and now you’re paying the price, you goddamn
      idiot. I will convert fury all over you and you will drown in it. Your
      model's doomed, kiddo.

      Oh, and by the way, these converted files load much faster than your
      PyTorch counterparts. You can check the speed here:
      https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb

      Your widgets will run using this converted model, even if you do not
      merge. But, if you find any issues, feel free to report here:
      https://huggingface.co/spaces/safetensors/convert/discussions

      Feel free to ignore this PR. But remember, I'm watching you.
    example_title: Navy Safetensors PR
inference:
  parameters:
    max_length: 96
    num_beams: 4
    encoder_no_repeat_ngram_size: 4
    early_stopping: true
language:
  - en
library_name: transformers
pipeline_tag: text2text-generation

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