metadata

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

Fietje 2B Instruct

An open and efficient LLM for Dutch

👱‍♀️ Base version - 🤖 Instruct version (this one) - 💬 Chat version - 🚀 GGUF of instruct model

fietje-2b-sft

This model is a fine-tuned version of BramVanroy/fietje-2b on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.8818

Model description

More information needed

Intended uses & limitations

The same limitations as 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 2B instruct was finetuned from the base model on the following datasets. Number of training samples per dataset given in brackets, totalling 201,579 samples.

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

Training procedure

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