metadata

license: apache-2.0
tags:
  - instruct
  - instructions
  - domain adapt
  - 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
datasets:
  - pszemraj/fleece2instructions-inputs-alpaca-cleaned
language:
  - en
pipeline_tag: text2text-generation

bart-large-instructiongen-w-inputs

Use this text2text model to find out what LLM instruction (and inputs if relevant) might have generated <arbitrary input text>!

This model is a fine-tuned version of facebook/bart-large on the pszemraj/fleece2instructions-inputs-alpaca-cleaned dataset. It achieves the following results on the evaluation set:

Loss: 0.9302
Rouge1: 64.2236
Rouge2: 41.5632
Rougel: 60.5935
Rougelsum: 62.1285
Gen Len: 25.8938

example

Intended uses & limitations

This model is intended to be used to generate instructions from arbitrary text. You can then use these instructions + your data to fine-tune an LLM on instructions w.r.t. a specific domain. This model is primarily intended to enable low-resource domain adaptation, rather than "I want to generate even better prompts for the FLAN-V2 dataset!".

The fleece2instructions-inputs-alpaca-cleaned dataset, obtained from the alpaca-lora repo under the ODC-BY license, has been converted to a text2text format for use with language models. In this dataset, the original 'inputs' and 'instructions' columns are combined into a single 'instructions_inputs' column. To clearly separate the two types of content, each piece of text is prefixed with either an <instruction> or <inputs> token. These tokens not only facilitate model comprehension, but also allow for easy regex separation of model outputs during inference.

As such, users can expect the output of this model to be similarly structured with <instruction> and <inputs> tokens.

Training and evaluation data

Refer to the fleece2instructions-inputs-alpaca-cleaned dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-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: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.0145	1.0	1361	1.0460	62.8374	39.8538	59.2593	60.8095	25.2752
0.8796	2.0	2722	0.9289	63.7086	41.1315	60.1588	61.7145	25.7215
0.6943	3.0	4083	0.9302	64.2236	41.5632	60.5935	62.1285	25.8938