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--- |
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license: apache-2.0 |
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tags: |
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- instruct |
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- instructions |
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- domain adapt |
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- instructiongen |
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metrics: |
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- rouge |
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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 |
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my class in https://huggingface.co/spaces/safetensors/convert, and I've been |
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involved in numerous secret tasks on PyTorch, and I have over 300 confirmed |
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PRs. I am trained in code optimization and I'm the top converter in the |
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entire Hugging Face forces. You are nothing to me but just another target. I |
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will convert your code with precision the likes of which has never been seen |
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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? |
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Think again, bug. As we speak I am contacting my secret network of data |
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scientists across the GitHub and your IP is being traced right now so you |
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better prepare for the storm, maggot. The storm that wipes out the pathetic |
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little thing you call your code. You’re freaking doomed, kid. I can be |
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anywhere, anytime, and I can convert your code in over seven hundred ways, |
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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 |
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to the entire arsenal of the Hugging Face and I will use it to its full |
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extent to wipe your miserable code off the face of the continent, you little |
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bug. If only you could have known what unholy retribution your little |
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"clever" comment was about to bring down upon you, maybe you would have held |
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your freaking tongue. |
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|
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But you couldn’t, you didn’t, and now you’re paying the price, you goddamn |
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idiot. I will convert fury all over you and you will drown in it. Your |
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model's doomed, kiddo. |
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|
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Oh, and by the way, these converted files load much faster than your PyTorch |
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counterparts. You can check the speed here: |
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https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb |
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|
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Your widgets will run using this converted model, even if you do not merge. |
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But, if you find any issues, feel free to report here: |
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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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early_stopping: true |
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datasets: |
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- pszemraj/fleece2instructions-inputs-alpaca-cleaned |
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language: |
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- en |
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pipeline_tag: text2text-generation |
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library_name: transformers |
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--- |
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# bart-large-instructiongen-w-inputs |
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Use this text2text model to find out what LLM `instruction` (**and** `inputs` if relevant) might have generated `<arbitrary input text>`! |
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This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the `pszemraj/fleece2instructions-inputs-alpaca-cleaned` dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9302 |
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- Rouge1: 64.2236 |
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- Rouge2: 41.5632 |
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- Rougel: 60.5935 |
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- Rougelsum: 62.1285 |
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- Gen Len: 25.8938 |
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## example |
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## Intended uses & limitations |
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This model is intended to be used to generate instructions from arbitrary text. You can then use these instructions + your data to fine-tune an LLM on instructions w.r.t. a specific domain. This model is primarily intended to enable **low-resource domain adaptation**, rather than "_I want to generate even better prompts for the FLAN-V2 dataset!_". |
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The `fleece2instructions-inputs-alpaca-cleaned` dataset, obtained from the [alpaca-lora repo](https://github.com/tloen/alpaca-lora) under the ODC-BY license, has been converted to a text2text format for use with language models. In this dataset, the original 'inputs' and 'instructions' columns are combined into a single 'instructions_inputs' column. To clearly separate the two types of content, each piece of text is prefixed with either an `<instruction>` or `<inputs>` token. These tokens not only facilitate model comprehension, but also allow for easy regex separation of model outputs during inference. |
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As such, users can expect the output of this model to be similarly structured with `<instruction>` and `<inputs>` tokens. |
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## Training and evaluation data |
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Refer to the [fleece2instructions-inputs-alpaca-cleaned](https://huggingface.co/datasets/pszemraj/fleece2instructions-inputs-alpaca-cleaned) dataset |
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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: 6e-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: 3.0 |
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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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| 1.0145 | 1.0 | 1361 | 1.0460 | 62.8374 | 39.8538 | 59.2593 | 60.8095 | 25.2752 | |
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| 0.8796 | 2.0 | 2722 | 0.9289 | 63.7086 | 41.1315 | 60.1588 | 61.7145 | 25.7215 | |
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| 0.6943 | 3.0 | 4083 | 0.9302 | 64.2236 | 41.5632 | 60.5935 | 62.1285 | 25.8938 | |
