---
license: mit
base_model: BramVanroy/fietje-2
tags:
- trl
- fietje
- alignment-handbook
- sft
datasets:
- BramVanroy/ultrachat_200k_dutch
- BramVanroy/no_robots_dutch
- BramVanroy/belebele_dutch
model-index:
- name: fietje-2-instruct
  results: []
pipeline_tag: text-generation
inference: false
language:
- nl
---

<p align="center" style="margin:0;padding:0">
  <img src="https://huggingface.co/BramVanroy/fietje-2-instruct/resolve/main/img/fietje-2b-banner-rounded.png" alt="Fietje banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
</p>

<div style="margin:auto; text-align:center">
  <h1 style="margin-bottom: 0">Fietje 2 Instruct</h1>
  <em>An open and efficient LLM for Dutch</em>
</div>

<blockquote class="tip" style="padding: 1.5em; border: 0">
  <p align="center" style="text-align: center; margin: 0">
    <a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2">👱‍♀️ Base version</a> -
    <a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2-instruct">🤖 Instruct version</a> (this one) -
    <a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2-chat">💬 Chat version</a> -
    <a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2-chat-GGUF">🚀 GGUF of Instruct</a>
  </p>
  <p align="center" style="text-align: center; margin: 0">
    <a href="https://huggingface.co/spaces/BramVanroy/fietje-2b"><strong>Chat with Fietje here!</strong></a>
  </p>
</blockquote>

This is the instruct version of Fietje, an SFT-tuned (instruction-tuned) variant of [the base model](https://huggingface.co/BramVanroy/fietje-2). Fietje is an adapated version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2), tailored to Dutch text generation by training on 28B tokens. It is small and efficient with a size of 2.7 billion parameters while performing almost on par with more powerful Dutch LLMs of twice its size like [GEITje 7B Ultra](https://huggingface.co/BramVanroy/GEITje-7B-ultra).

A thorough description of the creation and evaluation of Fietje as well as usage examples are available in [this Github repository](https://github.com/BramVanroy/fietje).

## Intended uses & limitations

The same limitations as [phi-2](https://huggingface.co/microsoft/phi-2#limitations-of-phi-2), and LLMs in general, apply here. LLMs hallucinate, make mistakes, and should not be trusted. Use at your own risk!

## Training and evaluation data

Fietje 2 instruct was finetuned from [the base model](https://huggingface.co/BramVanroy/fietje-2) on the following datasets. Number of training samples per dataset given in brackets, totalling 201,579 samples.

- [BramVanroy/ultrachat_200k_dutch](https://huggingface.co/datasets/BramVanroy/ultrachat_200k_dutch): gpt-4-1106-preview; multi-turn; fully generated (192,598)
- [BramVanroy/no_robots_dutch](https://huggingface.co/datasets/BramVanroy/no_robots_dutch): gpt-4-1106-preview; prompt translate, answer generated; some items have system messages (8181)
- [BramVanroy/belebele_dutch](https://huggingface.co/datasets/BramVanroy/belebele_dutch): Dutch portion of [belebele](https://huggingface.co/datasets/facebook/belebele), formatted into SFT format (800)

## Training procedure

I am thankful to the [Flemish Supercomputer Center](https://www.vscentrum.be/) (VSC) for providing the computational power to accomplish this project. Accounting for waiting for jobs, training took around a day on four nodes of 4x A100 80GB each (16 total). I cannot find the exact time anymore and I do not think that the runtime in `all_results.json` accounts for interrupted-and-continued jobs.

Training was done with the wonderful [alignment-handbook](https://github.com/huggingface/alignment-handbook), using DeepSpeed as a back-end. Exact training recipes and SLURM script are given in the [Github repository](https://github.com/BramVanroy/fietje).


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 42
- eval_batch_size: 42
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 672
- total_eval_batch_size: 672
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9325        | 1.0   | 178  | 0.9060          |
| 0.8687        | 2.0   | 356  | 0.8850          |
| 0.8385        | 3.0   | 534  | 0.8818          |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2