Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 15
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("birgermoell/zebra")
model = AutoModelForCausalLM.from_pretrained("birgermoell/zebra")Quick Links
zebra
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
Merge Details
Merge Method
This model was merged using the Model Stock merge method using mlabonne/Zebrafish-7B as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: birgermoell/Munin-NeuralBeagle-NorskGPT
- model: mlabonne/Zebrafish-7B
- model: mlabonne/AlphaMonarch-7B
merge_method: model_stock
base_model: mlabonne/Zebrafish-7B
dtype: bfloat16
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="birgermoell/zebra")