DaMorph
Collection
DaMorph is a collection of experimental models developed to explore the impact of morphological segmentation on Danish NLP. • 8 items • Updated
Text Generation
Transformers
Safetensors
gpt2
Danish
Mixed Tokenization
CerebrasGPT
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("RyeAI/DA-MIXED-CEREBRAS")
model = AutoModelForCausalLM.from_pretrained("RyeAI/DA-MIXED-CEREBRAS")Quick Links
_______ ___ .___ ___. ______ .______ .______ __ __
| \ / \ | \/ | / __ \ | _ \ | _ \ | | | |
| .--. | / ^ \ | \ / | | | | | | |_) | | |_) | | |__| |
| | | | / /_\ \ | |\/| | | | | | | / | ___/ | __ |
| '--' | / _____ \ | | | | | `--' | | |\ \----.| | | | | |
|_______/ /__/ \__\ |__| |__| \______/ | _| `._____|| _| |__| |__|
DA-MIXED-CEREBRAS
This is an experimental Danish language model fine-tuned on a combination of tokenizers, including both morphological and BPE approaches. Built on the CerebrasGPT-111M architecture, it explores how mixed tokenization strategies affect Danish text generation.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RyeAI/DA-MIXED-CEREBRAS")