---
language:
- code
license: openrail
tags:
- generated_from_trainer
datasets:
- bigcode/the-stack-dedup
pipeline_tag: text-generation
base_model: bigcode/santacoder
model-index:
- name: santacoder-finetuned-the-stack-swift
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# SantaCoder 🎅 fine-tuned on Swift 🍏

This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8353

## Model description

The [SantaCoder](https://huggingface.co/bigcode/santacoder) models are a series of 1.1B parameter models trained on the Python, Java, and JavaScript subset of [The Stack (v1.1)](https://huggingface.co/datasets/bigcode/the-stack) (which excluded opt-out requests). 
The main model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255).
In addition, there are several models that were trained on datasets with different filter parameters and with architecture and objective variations. 

## Intended uses & limitations

More information needed

## Training and evaluation data

The Stack contains over 6TB of permissively-licensed source code files covering 358 programming languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. **This is the near-deduplicated version with 3TB data.**

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1132        | 0.05  | 500   | 1.0496          |
| 1.0077        | 0.1   | 1000  | 1.0245          |
| 1.0109        | 0.15  | 1500  | 1.0111          |
| 1.1106        | 0.2   | 2000  | 1.0025          |
| 0.5083        | 0.25  | 2500  | 1.0163          |
| 0.2996        | 0.3   | 3000  | 1.0339          |
| 1.0745        | 0.35  | 3500  | 0.9682          |
| 1.0355        | 0.4   | 4000  | 0.9467          |
| 0.9156        | 0.45  | 4500  | 0.9229          |
| 0.8834        | 0.5   | 5000  | 0.9199          |
| 0.6363        | 0.55  | 5500  | 0.9048          |
| 0.8771        | 0.6   | 6000  | 0.8899          |
| 1.9208        | 0.65  | 6500  | 0.8727          |
| 0.8816        | 0.7   | 7000  | 0.8633          |
| 0.8918        | 0.75  | 7500  | 0.8543          |
| 0.8714        | 0.8   | 8000  | 0.8454          |
| 0.9486        | 0.85  | 8500  | 0.8402          |
| 1.0609        | 0.9   | 9000  | 0.8364          |
| 0.9124        | 0.95  | 9500  | 0.8356          |
| 0.9743        | 1.0   | 10000 | 0.8353          |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2

### Citation
```
@misc {manuel_romero_2023,
	author       = { {Manuel Romero} },
	title        = { santacoder-finetuned-the-stack-swift (Revision 99b9470) },
	year         = 2023,
	url          = { https://huggingface.co/mrm8488/santacoder-finetuned-the-stack-swift },
	doi          = { 10.57967/hf/0348 },
	publisher    = { Hugging Face }
}
```