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README.md
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metrics:
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- rouge
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model-index:
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- name: flan-t5-small-
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# flan-t5-small-
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
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It achieves the following results on the evaluation set:
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metrics:
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- rouge
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model-index:
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- name: flan-t5-small-instructiongen
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results: []
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widget:
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- text: >-
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To plan a successful surprise birthday party, you'll need to start by
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choosing the right venue. Consider the type of atmosphere and the size of
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the area that will be suitable for the number of guests you plan to
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invite. Choose the right decorations based on your brother's interests,
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such as balloons in his favorite colors, banners, and streamers. Next,
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decide on the food and drinks, making sure they are tasty and appropriate
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for the occasion. Then decide on the other games, music, and entertainment
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that will make the party memorable. Finally, involve your brother's
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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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To create a miniature diorama of a post-apocalyptic cityscape, start by
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selecting a scale model of a building that fits the theme. Use a hobby
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knife and glue to cut and assemble the model into a ruined or abandoned
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version of itself, adding details like broken windows and graffiti. Create
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a base for the diorama using foam, plaster, or other materials, and paint
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it to resemble a ruined street or sidewalk. Add miniature vehicles,
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debris, and figures to complete the scene, and use weathering techniques
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like dry brushing and rust washes to add realism. Display the diorama in a
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shadow box or other protective case to showcase your work.
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example_title: Miniature diorama creation
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- text: >-
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To create a costume inspired by the world of cyberpunk, start by selecting
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clothing that is futuristic and edgy, such as leather jackets,
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neon-colored accessories, and tech-inspired patterns. Add accessories like
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goggles, cybernetic implants, and LED lights to enhance the cyberpunk
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vibe. Use makeup and body paint to create a futuristic look, such as
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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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To create a surreal landscape using 3D software, start by creating a base
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terrain with mountains, valleys, and other natural features. Use fractal
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noise and displacement mapping to add texture and detail to the terrain,
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and experiment with different materials like rock, grass, and water. Add
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surreal elements like floating islands, giant mushrooms, or impossible
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geometry to create a dreamlike atmosphere. Use lighting and color grading
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to enhance the mood and tone of the scene, and render the final image at a
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high resolution for maximum impact. Share your surreal landscape with the
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world and inspire others to explore the possibilities of 3D art.
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example_title: Surreal 3D landscape creation
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- text: >-
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To train for a marathon, start by setting a realistic goal and creating a
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training plan. Build up your mileage gradually over time, and incorporate
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cross-training and strength exercises to prevent injury and improve
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endurance. Be sure to stay hydrated and properly fuel your body with
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nutritious foods. Listen to your body and adjust your training as needed
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to avoid overexertion or burnout. Finally, taper your training in the
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weeks leading up to the race to give your body time to rest and recover
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before the big day.
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example_title: Marathon training
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---
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# flan-t5-small-instructiongen
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
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It achieves the following results on the evaluation set:
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