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README.md

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+---
+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.