README.md

---
license: apache-2.0
tags:
- self-instruct
- instruction generation
- instructiongen
datasets:
- pszemraj/fleece2instructions
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
base_model: google/flan-t5-base
model-index:
- name: flan-t5-base-instructiongen
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: pszemraj/fleece2instructions
      type: pszemraj/fleece2instructions
      split: validation
    metrics:
    - type: rouge
      value: 58.9516
      name: Rouge1
---


# flan-t5-base-instructiongen

Instead of generating questions from text, generate instructions for LLMs!

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co./google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0642
- Rouge1: 58.9516
- Rouge2: 41.8006
- Rougel: 56.8249
- Rougelsum: 56.9171
- Gen Len: 13.1493

## Intended uses & limitations

> Of the three models fine-tuned so far, `flan-t5-base` is in an awkward position where it has the largest model file size, but not the best performance. I'd recommend looking at the two linked below.

This is just a `base` FLAN model, and is mostly uploaded for comparison with the [FLAN-small](https://huggingface.co./pszemraj/flan-t5-small-instructiongen) and [bart-base](https://huggingface.co./pszemraj/bart-base-instructiongen) variants.

Additionally, it 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 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: 2.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.1939        | 1.0   | 362  | 1.0822          | 58.1758 | 40.9388 | 56.1219 | 56.2464   | 13.2592 |
| 1.1667        | 2.0   | 724  | 1.0642          | 58.9516 | 41.8006 | 56.8249 | 56.9171   | 13.1493 |