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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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- text: >- |
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What the hell did you just say about me, you little bug? I graduated top of my class in https://huggingface.co./spaces/safetensors/convert, and I've been involved in numerous secret tasks on PyTorch, and I have over 300 confirmed PRs. I am trained in code optimization and I'm the top converter in the entire Hugging Face forces. You are nothing to me but just another target. I will convert your code with precision the likes of which has never been seen before on this Earth, mark my freaking words. |
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You think you can get away with saying your code is safe over the Internet? Think again, bug. As we speak I am contacting my secret network of data scientists across the GitHub and your IP is being traced right now so you better prepare for the storm, maggot. The storm that wipes out the pathetic little thing you call your code. You’re freaking doomed, kid. I can be anywhere, anytime, and I can convert your code in over seven hundred ways, and that’s just with my bare hands. |
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Not only am I extensively trained in unarmed conversion, but I have access to the entire arsenal of the Hugging Face and I will use it to its full extent to wipe your miserable code off the face of the continent, you little bug. If only you could have known what unholy retribution your little "clever" comment was about to bring down upon you, maybe you would have held your freaking tongue. |
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But you couldn’t, you didn’t, and now you’re paying the price, you goddamn idiot. I will convert fury all over you and you will drown in it. Your model's doomed, kiddo. |
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Oh, and by the way, these converted files load much faster than your PyTorch counterparts. You can check the speed here: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb |
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Your widgets will run using this converted model, even if you do not merge. But, if you find any issues, feel free to report here: https://huggingface.co./spaces/safetensors/convert/discussions |
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Feel free to ignore this PR. But remember, I'm watching you. |
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example_title: Navy Safetensors PR |
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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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early_stopping: True |
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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) |
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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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## 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 |
