metadata
license: apache-2.0
tags:
- generated_from_trainer
- alpaca
- self-instruct
- instruction generation
- instructiongen
datasets:
- pszemraj/fleece2instructions
metrics:
- rouge
model-index:
- name: bart-base-instructiongen
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pszemraj/fleece2instructions
type: pszemraj/fleece2instructions
split: validation
metrics:
- name: Rouge1
type: rouge
value: 61.7209
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
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.
About
Hypothesis: Apply text-to-text models to unlabeled domain-specific text to generate appropriate LLM instructions. Consequently, this may enable domain adaptation of instruction-tuned LLMs, making them more versatile for specific domains.
This model is a fine-tuned version of the facebook/bart-base model, fine-tuned using the pszemraj/fleece2instructions dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0034
- Rouge1: 61.7209
- Rouge2: 45.0116
- Rougel: 59.8188
- Rougelsum: 59.8931
- Gen Len: 14.3179
Intended uses & limitations
This is just a base model/example. There is likely to be even better performance with larger models (click here to see other checkpoints)
Additionally, this was trained on a dataset of only instructions+outputs, with the inputs filtered out. This means that text of 1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo will not get you "Rank the following ice cream flavors: oreo, mint chip, chocolate chip, cookies and cream".
Training and evaluation data
See the linked dataset pszemraj/fleece2instructions - it is a filtered/formatted version of tatsu-lab/alpaca to generate instructions for arbitrary text.
- Some of the API examples are intentionally weird to demonstrate the generalizability of the model.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- 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 |
|---|---|---|---|---|---|---|---|---|
| 1.2723 | 1.0 | 362 | 1.0325 | 61.6206 | 45.1199 | 59.6467 | 59.7534 | 14.0443 |
| 1.0157 | 2.0 | 724 | 1.0034 | 62.4433 | 46.0114 | 60.5355 | 60.6392 | 14.1807 |