

# bart-large-instructiongen-w-inputs

Use this text2text model to find out what LLM `instruction` (**and** `inputs` if relevant) might have generated `<arbitrary input text>`!

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.
It achieves the following results on the evaluation set:
- Loss: 0.9302
- Rouge1: 64.2236
- Rouge2: 41.5632
- Rougel: 60.5935
- Rougelsum: 62.1285
- Gen Len: 25.8938

## example 

![api](https://i.imgur.com/2xubG7N.png)

## Intended uses & limitations

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!_".

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. 

As such, users can expect the output of this model to be similarly structured with `<instruction>` and `<inputs>` tokens.

## Training and evaluation data

Refer to the [fleece2instructions-inputs-alpaca-cleaned](https://huggingface.co/datasets/pszemraj/fleece2instructions-inputs-alpaca-cleaned) dataset

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.0145        | 1.0   | 1361 | 1.0460          | 62.8374 | 39.8538 | 59.2593 | 60.8095   | 25.2752 |
| 0.8796        | 2.0   | 2722 | 0.9289          | 63.7086 | 41.1315 | 60.1588 | 61.7145   | 25.7215 |
| 0.6943        | 3.0   | 4083 | 0.9302          | 64.2236 | 41.5632 | 60.5935 | 62.1285   | 25.8938 |
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["instruct", "instructions", "domain adapt", "instructiongen"], "datasets": ["pszemraj/fleece2instructions-inputs-alpaca-cleaned"], "metrics": ["rouge"], "widget": [{"text": "You'll need to start by choosing the right venue. Consider the type of atmosphere and the size of the area that will be suitable for the number of guests you plan to invite. Choose the right decorations based on your brother's interests, such as balloons in his favorite colors, banners, and streamers. Next, decide on the food and drinks, making sure they are tasty and appropriate for the occasion. Then decide on the other games, music, and entertainment that will make the party memorable. Finally, involve your brother's friends and family to help create the perfect surprise.", "example_title": "birthday party"}, {"text": "1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo", "example_title": "ice cream"}, {"text": "Start by selecting a scale model of a building that fits the theme. Use a hobby knife and glue to cut and assemble the model into a ruined or abandoned version of itself, adding details like broken windows and graffiti. Create a base for the diorama using foam, plaster, or other materials, and paint it to resemble a ruined street or sidewalk. Add miniature vehicles, debris, and figures to complete the scene, and use weathering techniques like dry brushing and rust washes to add realism. Display the diorama in a shadow box or other protective case to showcase your work.", "example_title": "Miniature diorama creation"}, {"text": "Start by selecting clothing that is futuristic and edgy, such as leather jackets, neon-colored accessories, and tech-inspired patterns. Add accessories like goggles, cybernetic implants, and LED lights to enhance the cyberpunk vibe. Use makeup and body paint to create a futuristic look, such as metallic skin or neon makeup. Consider adding functional elements to your costume, such as a built-in backpack or hidden pockets for your tech gadgets. Finally, practice your confident walk and embrace your inner cyberpunk for a memorable and immersive costume experience.", "example_title": "Cyberpunk costume design"}, {"text": "Start by creating a base terrain with mountains, valleys, and other natural features. Use fractal noise and displacement mapping to add texture and detail to the terrain, and experiment with different materials like rock, grass, and water. Add surreal elements like floating islands, giant mushrooms, or impossible geometry to create a dreamlike atmosphere. Use lighting and color grading to enhance the mood and tone of the scene, and render the final image at a high resolution for maximum impact. Share your surreal landscape with the world and inspire others to explore the possibilities of 3D art.", "example_title": "Surreal 3D landscape creation"}, {"text": "Start by setting a realistic goal and creating a training plan. Build up your mileage gradually over time, and incorporate cross-training and strength exercises to prevent injury and improve endurance. Be sure to stay hydrated and properly fuel your body with nutritious foods. Listen to your body and adjust your training as needed to avoid overexertion or burnout. Finally, taper your training in the weeks leading up to the race to give your body time to rest and recover before the big day.", "example_title": "Marathon training"}, {"text": "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. \nYou 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\u2019re freaking doomed, kid. I can be anywhere, anytime, and I can convert your code in over seven hundred ways, and that\u2019s just with my bare hands.\nNot 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. \nBut you couldn\u2019t, you didn\u2019t, and now you\u2019re paying the price, you goddamn idiot. I will convert fury all over you and you will drown in it. Your model's doomed, kiddo.\nOh, 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\nYour 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\nFeel free to ignore this PR. But remember, I'm watching you.", "example_title": "Navy Safetensors PR"}], "inference": {"parameters": {"max_length": 96, "num_beams": 4, "early_stopping": true}}, "pipeline_tag": "text2text-generation"}
transformers pytorch onnx safetensors bart text2text-generation instruct instructions domain adapt instructiongen en dataset:pszemraj/fleece2instructions-inputs-alpaca-cleaned doi:10.57967/hf/0485 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #safetensors #bart #text2text-generation #instruct #instructions #domain adapt #instructiongen #en #dataset-pszemraj/fleece2instructions-inputs-alpaca-cleaned #doi-10.57967/hf/0485 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bart-large-instructiongen-w-inputs
==================================


Use this text2text model to find out what LLM 'instruction' (and 'inputs' if relevant) might have generated ''!


This model is a fine-tuned version of facebook/bart-large on the 'pszemraj/fleece2instructions-inputs-alpaca-cleaned' dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9302
* Rouge1: 64.2236
* Rouge2: 41.5632
* Rougel: 60.5935
* Rougelsum: 62.1285
* Gen Len: 25.8938


example
-------


!api


Intended uses & limitations
---------------------------


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!*".


The 'fleece2instructions-inputs-alpaca-cleaned' dataset, obtained from the alpaca-lora repo 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 '' or '' token. These tokens not only facilitate model comprehension, but also allow for easy regex separation of model outputs during inference.


As such, users can expect the output of this model to be similarly structured with '' and '' tokens.


Training and evaluation data
----------------------------


Refer to the fleece2instructions-inputs-alpaca-cleaned dataset


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.03
* num\_epochs: 3.0


### Training results




### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.03
* num\_epochs: 3.0 ### Training results
TAGS
#transformers #pytorch #onnx #safetensors #bart #text2text-generation #instruct #instructions #domain adapt #instructiongen #en #dataset-pszemraj/fleece2instructions-inputs-alpaca-cleaned #doi-10.57967/hf/0485 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.03
* num\_epochs: 3.0 ### Training results
TAGS
#transformers #pytorch #onnx #safetensors #bart #text2text-generation #instruct #instructions #domain adapt #instructiongen #en #dataset-pszemraj/fleece2instructions-inputs-alpaca-cleaned #doi-10.57967/hf/0485 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.03
* num\_epochs: 3.0### Training results