84 lines
3.1 KiB
Markdown
84 lines
3.1 KiB
Markdown
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---
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base_model: KennethTM/gpt2-small-danish
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datasets:
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- oscar
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inference: false
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language:
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- da
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model_creator: KennethTM
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model_name: gpt2-small-danish
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pipeline_tag: text-generation
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quantized_by: afrideva
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tags:
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- gguf
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- ggml
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- quantized
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- q2_k
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- q3_k_m
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- q4_k_m
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- q5_k_m
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- q6_k
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- q8_0
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widget:
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- text: Der var engang
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---
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# KennethTM/gpt2-small-danish-GGUF
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Quantized GGUF model files for [gpt2-small-danish](https://huggingface.co/KennethTM/gpt2-small-danish) from [KennethTM](https://huggingface.co/KennethTM)
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [gpt2-small-danish.fp16.gguf](https://huggingface.co/afrideva/gpt2-small-danish-GGUF/resolve/main/gpt2-small-danish.fp16.gguf) | fp16 | 328.21 MB |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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## Original Model Card:
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# What is this?
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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.
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# How to use
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Test the model using the pipeline from the [🤗 Transformers](https://github.com/huggingface/transformers) library:
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```python
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from transformers import pipeline
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generator = pipeline("text-generation", model = "KennethTM/gpt2-small-danish")
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text = generator("Manden arbejdede som")
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print(text[0]["generated_text"])
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```
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Or load it using the Auto* classes:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("KennethTM/gpt2-small-danish")
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model = AutoModelForCausalLM.from_pretrained("KennethTM/gpt2-small-danish")
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```
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# Model training
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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.
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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).
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Initially, only the word token embeddings are trained using 50.000 samples. Finally, the whole model is trained using 1.000.000 samples.
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For reference, the model achieves a perplexity of 33.5 on 5.000 random validation samples.
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Model training is carried out on an 8 GB GPU.
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# Notes
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This is a pre-trained model, for optimal performance it should be finetuned for new tasks.
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