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Model Details

Finetune of gpt-sw3-6.7b-v2 using LoRA with 4-bit quantization. Adapters merged with base model, however with bfloat16 precision tensors.

Usage:

This is a finetune experiment. How to use will be provided later.

Evaluation

ScandEval benchmarks:

Dataset Performance (Metric 1 / Metric 2)
swerec 74.95 ± 1.17 / 61.38 ± 1.37
suc3 30.75 ± 4.11 / 25.69 ± 4.83
scala-sv 8.96 ± 2.09 / 51.50 ± 2.94
scandiqa-sv 50.71 ± 0.99 / 56.76 ± 0.89
swedn 64.37 ± 0.72 / 18.25 ± 0.29
mmlu-sv 5.45 ± 0.91 / 28.14 ± 0.82
hellaswag-sv 27.95 ± 0.73 / 4.19 ± 0.94
speed 5322.20 ± 1132.75 / 1280.06 ± 408.08
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Model size
6.71B params
Tensor type
BF16
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Finetuned from

Dataset used to train skvarre/gpt-sw3-6.7b-v2-instruct-slimorcasv