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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- alpaca |
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- self-instruct |
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- instruction generation |
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- instructiongen |
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datasets: |
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- pszemraj/fleece2instructions |
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metrics: |
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- rouge |
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model-index: |
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- name: bart-base-instructiongen |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: pszemraj/fleece2instructions |
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type: pszemraj/fleece2instructions |
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split: validation |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 61.7209 |
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widget: |
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- text: >- |
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You'll need to start by choosing the right venue. Consider the type of |
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atmosphere and the size of the area that will be suitable for the number of |
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guests you plan to invite. Choose the right decorations based on your |
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brother's interests, such as balloons in his favorite colors, banners, and |
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streamers. Next, decide on the food and drinks, making sure they are tasty |
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and appropriate for the occasion. Then decide on the other games, music, and |
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entertainment that will make the party memorable. Finally, involve your |
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brother's friends and family to help create the perfect surprise. |
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example_title: birthday party |
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- text: 1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo |
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example_title: ice cream |
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- text: >- |
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Start by selecting a scale model of a building that fits the theme. Use a |
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hobby knife and glue to cut and assemble the model into a ruined or |
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abandoned version of itself, adding details like broken windows and |
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graffiti. Create a base for the diorama using foam, plaster, or other |
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materials, and paint it to resemble a ruined street or sidewalk. Add |
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miniature vehicles, debris, and figures to complete the scene, and use |
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weathering techniques like dry brushing and rust washes to add realism. |
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Display the diorama in a shadow box or other protective case to showcase |
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your work. |
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example_title: Miniature diorama creation |
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- text: >- |
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Start by selecting clothing that is futuristic and edgy, such as leather |
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jackets, neon-colored accessories, and tech-inspired patterns. Add |
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accessories like goggles, cybernetic implants, and LED lights to enhance the |
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cyberpunk vibe. Use makeup and body paint to create a futuristic look, such |
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as metallic skin or neon makeup. Consider adding functional elements to your |
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costume, such as a built-in backpack or hidden pockets for your tech |
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gadgets. Finally, practice your confident walk and embrace your inner |
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cyberpunk for a memorable and immersive costume experience. |
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example_title: Cyberpunk costume design |
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- text: >- |
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Start by creating a base terrain with mountains, valleys, and other natural |
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features. Use fractal noise and displacement mapping to add texture and |
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detail to the terrain, and experiment with different materials like rock, |
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grass, and water. Add surreal elements like floating islands, giant |
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mushrooms, or impossible geometry to create a dreamlike atmosphere. Use |
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lighting and color grading to enhance the mood and tone of the scene, and |
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render the final image at a high resolution for maximum impact. Share your |
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surreal landscape with the world and inspire others to explore the |
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possibilities of 3D art. |
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example_title: Surreal 3D landscape creation |
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- text: >- |
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Start by setting a realistic goal and creating a training plan. Build up |
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your mileage gradually over time, and incorporate cross-training and |
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strength exercises to prevent injury and improve endurance. Be sure to stay |
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hydrated and properly fuel your body with nutritious foods. Listen to your |
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body and adjust your training as needed to avoid overexertion or burnout. |
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Finally, taper your training in the weeks leading up to the race to give |
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your body time to rest and recover before the big day. |
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example_title: Marathon training |
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inference: |
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parameters: |
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max_length: 96 |
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num_beams: 4 |
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--- |
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# bart-base-instructiongen |
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Instead of generating questions from text, generate instructions for LLMs! |
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- Check out a [basic demo on Spaces](https://huggingface.co/spaces/pszemraj/generate-instructions) |
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- An example of how to use instructiongen models in a CLI script can be found [here](https://gist.github.com/pszemraj/8b0213e700763106074d3ac15d041c14) |
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- You can find other models fine-tuned for instruction generation by [searching for the instructiongen tag](https://huggingface.co/models?other=instructiongen). |
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## About |
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**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. |
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This model is a fine-tuned version of the [facebook/bart-base](https://huggingface.co/facebook/bart-base) model, fine-tuned using the `pszemraj/fleece2instructions` dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0034 |
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- Rouge1: 61.7209 |
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- Rouge2: 45.0116 |
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- Rougel: 59.8188 |
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- Rougelsum: 59.8931 |
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- Gen Len: 14.3179 |
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## Intended uses & limitations |
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This is just a base model/example. There is likely to be even better performance with larger models (click [here to see other checkpoints](https://huggingface.co/models?other=instructiongen)) |
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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"*. |
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## Training and evaluation data |
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See the linked dataset `pszemraj/fleece2instructions` - it is a filtered/formatted version of `tatsu-lab/alpaca` to generate instructions for arbitrary text. |
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- Some of the API examples are intentionally weird to demonstrate the generalizability of the model. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.02 |
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- num_epochs: 2.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 1.2723 | 1.0 | 362 | 1.0325 | 61.6206 | 45.1199 | 59.6467 | 59.7534 | 14.0443 | |
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| 1.0157 | 2.0 | 724 | 1.0034 | 62.4433 | 46.0114 | 60.5355 | 60.6392 | 14.1807 | |
