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---
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
early_stopping: true
datasets:
- pszemraj/fleece2instructions-inputs-alpaca-cleaned
language:
- en
pipeline_tag: text2text-generation
library_name: transformers
---
# bart-base-instructiongen-w-inputs
Use this text2text model to find out what LLM `instruction` (**and** `inputs` if relevant) might have generated `<arbitrary input text>`!
- Check out a [basic demo on Spaces](https://huggingface.co/spaces/pszemraj/generate-instructions)
- An example of how to use instructiongen models in a CLI script can be found [here](https://gist.github.com/pszemraj/8b0213e700763106074d3ac15d041c14)
- You can find other models fine-tuned for instruction generation by [searching for the instructiongen tag](https://huggingface.co/models?other=instructiongen)
## about
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the `pszemraj/fleece2instructions-inputs-alpaca-cleaned` dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9579
- Rouge1: 62.3604
- Rouge2: 39.5109
- Rougel: 58.8843
- Rougelsum: 60.4494
- Gen Len: 24.9917
## 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](https://github.com/tloen/alpaca-lora) 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.
This is just the base model, for better performance (but slower/compute intensive) see the [bart-large](https://huggingface.co/pszemraj/bart-large-instructiongen-w-inputs) version. Further exploration/data may lead to even better models!
## Training and evaluation data
Refer to the [fleece2instructions-inputs-alpaca-cleaned](https://huggingface.co/datasets/pszemraj/fleece2instructions-inputs-alpaca-cleaned) dataset
## 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: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.1147 | 1.0 | 680 | 0.9901 | 61.8451 | 38.8293 | 58.3372 | 59.8658 | 25.2401 |
| 0.9565 | 2.0 | 1360 | 0.9579 | 62.3604 | 39.5109 | 58.8843 | 60.4494 | 24.9917 | |