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--- |
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license: apache-2.0 |
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datasets: |
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- hakurei/open-instruct-v1 |
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tags: |
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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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- bart |
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- open-instruct |
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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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encoder_no_repeat_ngram_size: 4 |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text2text-generation |
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--- |
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# bart-base-open-instructiongen-v1 |
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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). 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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## Model description |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the hakurei/open-instruct-v1 dataset. |
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- This model **only** generates the `instruction` for arbitrary text (it **does not** provide `inputs` as well - look for models with `w-inputs` in the name). |
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- There was no validation split at the time of training, so no statistics here. |
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- Comparing the performance of this model with [pszemraj/bart-base-instructiongen](https://huggingface.co/pszemraj/bart-base-instructiongen) might give some indication of whether and how much dataset scaling is needed to produce "robust" instruction generators. |
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- If you notice any trends, feel free to reach out! would be happy to hear about it. |
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## Training and evaluation data |
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See `hakurei/open-instruct-v1`. This model was trained on the dataset "backwards", i.e. the model was given the `output` column as input and trained to predict `instruction`. |
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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: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 0.03 |
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- num_epochs: 2.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.28.0.dev0 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.9.0 |
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- Tokenizers 0.12.1 |
