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# storage/context

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the mhenrichsen/context-aware-splits-english dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0253

## Model description
- This model is used to split texts in a context aware way. Used for RAG applications.
- This model is an adapter for Mistral 7b.
It uses the Alpaca format:

```
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
Your task is to segment text into smaller blocks. Split the text where it makes sense and be vary of the context. The ideal split should be close to {WORD_COUNT} words.

### Input:
Q: Information/File Manager I'm looking for a file manager application which helps to organize a large amount of movies, pictures, music, text documents, databases, audio-books and ebooks. Right now I only use the Finder which doesn't work well, because I really need a function to put single files into multiple categories. Simply using the file system for this creates a confusing nesting of files. A: Depending on the number of categories you require to handle, you could always use a combination of the finder with the built in label functionality, thus a movie can be held in one area (movies directory, for example), but "tagged" as something else. Using smart directories and saved searches you can view your files by a combination of the attributes (location, label, media type) to create custom views. All without purchasing software. Cheap and cheerful, but may be suitable to your needs. A: Maybe use a file manager that supports Open Meta. Or use symbolic links for organizing all your media files. Or even use hardlinked files if you dare.

### Response:
```

Response:
```
{'splits': ["Q: Information/File Manager I'm looking for a file manager application which helps to organize a large amount of movies, pictures, music, text documents, databases, audio-books and ebooks. Right now I only use the Finder which doesn't work well, because I really need a function to put single files into multiple categories. Simply using the file system for this creates a confusing nesting of files.", 'A: Depending on the number of categories you require to handle, you could always use a combination of the finder with the built in label functionality, thus a movie can be held in one area (movies directory, for example), but "tagged" as something else. Using smart directories and saved searches you can view your files by a combination of the attributes (location, label, media type) to create custom views. All without purchasing software. Cheap and cheerful, but may be suitable to your needs.', 'A: Maybe use a file manager that supports Open Meta. Or use symbolic links for organizing all your media files. Or even use hardlinked files if you dare.'], 'topic': 'Discussion on file manager applications for organizing large amount of media files.'}
```



### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1557        | 0.01  | 1    | 0.1552          |
| 0.0936        | 0.05  | 6    | 0.0695          |
| 0.0447        | 0.1   | 12   | 0.0413          |
| 0.0347        | 0.16  | 18   | 0.0357          |
| 0.0314        | 0.21  | 24   | 0.0324          |
| 0.0306        | 0.26  | 30   | 0.0309          |
| 0.0276        | 0.31  | 36   | 0.0294          |
| 0.028         | 0.36  | 42   | 0.0284          |
| 0.0307        | 0.41  | 48   | 0.0281          |
| 0.0276        | 0.47  | 54   | 0.0274          |
| 0.0251        | 0.52  | 60   | 0.0267          |
| 0.0244        | 0.57  | 66   | 0.0269          |
| 0.0268        | 0.62  | 72   | 0.0263          |
| 0.0249        | 0.67  | 78   | 0.0262          |
| 0.0252        | 0.73  | 84   | 0.0258          |
| 0.0259        | 0.78  | 90   | 0.0257          |
| 0.0241        | 0.83  | 96   | 0.0255          |
| 0.0241        | 0.88  | 102  | 0.0254          |
| 0.0253        | 0.93  | 108  | 0.0254          |
| 0.0234        | 0.98  | 114  | 0.0253          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mhenrichsen/context-aware-splits-english"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "storage/context", "results": []}]}
transformers safetensors mistral text-generation generated_from_trainer en dataset:mhenrichsen/context-aware-splits-english base_model:mistralai/Mistral-7B-v0.1 license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference 4-bit region:us