---
base_model: KennethTM/gpt2-small-danish
datasets:
- oscar
inference: false
language:
- da
model_creator: KennethTM
model_name: gpt2-small-danish
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
widget:
- text: Der var engang
---
# KennethTM/gpt2-small-danish-GGUF

Quantized GGUF model files for [gpt2-small-danish](https://huggingface.co/KennethTM/gpt2-small-danish) from [KennethTM](https://huggingface.co/KennethTM)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [gpt2-small-danish.fp16.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.fp16.gguf) | fp16 | 328.21 MB  |
| [gpt2-small-danish.q2_k.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.q2_k.gguf) | q2_k | 81.30 MB  |
| [gpt2-small-danish.q3_k_m.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.q3_k_m.gguf) | q3_k_m | 95.56 MB  |
| [gpt2-small-danish.q4_k_m.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.q4_k_m.gguf) | q4_k_m | 110.27 MB  |
| [gpt2-small-danish.q5_k_m.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.q5_k_m.gguf) | q5_k_m | 124.20 MB  |
| [gpt2-small-danish.q6_k.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.q6_k.gguf) | q6_k | 136.02 MB  |
| [gpt2-small-danish.q8_0.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/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](https://github.com/huggingface/transformers) library:

```python
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:

```python
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](https://huggingface.co/datasets/oscar) ('unshuffled_deduplicated_da') and a context length of 1024 tokens.

The model weights are initialized from the English [GPT-2 small model](https://huggingface.co/gpt2) with new word token embeddings created for Danish using [WECHSEL](https://github.com/CPJKU/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.