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# Context aware splitter 1.1b

This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0598

## Model description
- This model is used to split texts in a context aware way. Used for RAG applications.
- This model is based off TinyLLaMA 1.1b
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.'}
```

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9849        | 0.03  | 1    | 2.0811          |
| 1.3107        | 0.17  | 5    | 1.1992          |
| 0.6399        | 0.34  | 10   | 0.6359          |
| 0.2779        | 0.51  | 15   | 0.2862          |
| 0.1807        | 0.68  | 20   | 0.1634          |
| 0.1256        | 0.85  | 25   | 0.1177          |
| 0.097         | 1.0   | 30   | 0.0891          |
| 0.1063        | 1.17  | 35   | 0.0734          |
| 0.0769        | 1.34  | 40   | 0.0723          |
| 0.0694        | 1.51  | 45   | 0.0633          |
| 0.0687        | 1.69  | 50   | 0.0624          |
| 0.0575        | 1.86  | 55   | 0.0622          |
| 0.0516        | 2.01  | 60   | 0.0609          |
| 0.0582        | 2.18  | 65   | 0.0603          |
| 0.0611        | 2.35  | 70   | 0.0600          |
| 0.0515        | 2.52  | 75   | 0.0598          |
| 0.0704        | 2.69  | 80   | 0.0598          |
| 0.0525        | 2.86  | 85   | 0.0598          |


### 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": "TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T", "model-index": [{"name": "Context aware splitter 1.1b", "results": []}]}
transformers pytorch safetensors llama text-generation generated_from_trainer en dataset:mhenrichsen/context-aware-splits-english base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us