README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - alpaca
  - self-instruct
  - instruction generation
  - instructiongen
metrics:
  - rouge
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
inference:
  parameters:
    max_length: 96
    num_beams: 4

bart-base-instructiongen-LongForm-r1

This model is a fine-tuned version of pszemraj/bart-base-instructiongen on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

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 results

Framework versions

• Transformers 4.29.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.12.0
• Tokenizers 0.13.3