---
base_model: bigcode/starcoderbase-1b
library_name: peft
license: bigcode-openrail-m
tags:
- generated_from_trainer
model-index:
- name: mathpaper
  results: []
---



# mathpaper

This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on a dataset of 1000 arxiv category theory publications.

## Model description

More information needed

## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- training_steps: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3831        | 0.05  | 100  | 1.4326          |
| 1.2475        | 0.1   | 200  | 1.4149          |
| 1.2937        | 0.15  | 300  | 1.3903          |
| 1.3187        | 0.2   | 400  | 1.3723          |
| 1.4185        | 0.25  | 500  | 1.3577          |
| 1.3816        | 0.3   | 600  | 1.3475          |
| 1.324         | 0.35  | 700  | 1.3467          |
| 1.3456        | 0.4   | 800  | 1.3347          |
| 1.2906        | 0.45  | 900  | 1.3360          |
| 1.2916        | 0.5   | 1000 | 1.3315          |
| 1.3851        | 0.55  | 1100 | 1.3232          |
| 1.1827        | 0.6   | 1200 | 1.3193          |
| 1.2704        | 0.65  | 1300 | 1.3180          |
| 1.2495        | 0.7   | 1400 | 1.3104          |
| 1.2986        | 0.75  | 1500 | 1.3059          |
| 1.3759        | 0.8   | 1600 | 1.3005          |
| 1.2775        | 0.85  | 1700 | 1.2983          |
| 1.2648        | 0.9   | 1800 | 1.2969          |
| 1.2247        | 0.95  | 1900 | 1.2961          |
| 1.2152        | 1.0   | 2000 | 1.2959          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1