KennethTM/gpt2-small-danish-GGUF

Quantized GGUF model files for gpt2-small-danish from KennethTM

Name	Quant method	Size
gpt2-small-danish.fp16.gguf	fp16	328.21 MB
gpt2-small-danish.q2_k.gguf	q2_k	81.30 MB
gpt2-small-danish.q3_k_m.gguf	q3_k_m	95.56 MB
gpt2-small-danish.q4_k_m.gguf	q4_k_m	110.27 MB
gpt2-small-danish.q5_k_m.gguf	q5_k_m	124.20 MB
gpt2-small-danish.q6_k.gguf	q6_k	136.02 MB
gpt2-small-danish.q8_0.gguf	q8_0	175.47 MB

Original Model Card:

What is this?

A GPT-2 model (small version, 124 M parameters) for Danish text generation. The model was not pre-trained from scratch but adapted from the English version.

How to use

Test the model using the pipeline from the 🤗 Transformers library:

from transformers import pipeline

generator = pipeline("text-generation", model = "KennethTM/gpt2-small-danish")
text = generator("Manden arbejdede som")

print(text[0]["generated_text"])

Or load it using the Auto* classes:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KennethTM/gpt2-small-danish")
model = AutoModelForCausalLM.from_pretrained("KennethTM/gpt2-small-danish")

Model training

The model is trained using the Danish part of the oscar dataset ('unshuffled_deduplicated_da') and a context length of 1024 tokens.

The model weights are initialized from the English GPT-2 small model with new word token embeddings created for Danish using WECHSEL.

Initially, only the word token embeddings are trained using 50.000 samples. Finally, the whole model is trained using 1.000.000 samples.

For reference, the model achieves a perplexity of 33.5 on 5.000 random validation samples.

Model training is carried out on an 8 GB GPU.

Notes

This is a pre-trained model, for optimal performance it should be finetuned for new tasks.