How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="KaraKaraWarehouse/UnFimbulvetr-20B")
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KaraKaraWarehouse/UnFimbulvetr-20B")
model = AutoModelForCausalLM.from_pretrained("KaraKaraWarehouse/UnFimbulvetr-20B", device_map="auto")
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UnFimbulvetr-20B

Waifu to catch your attention

This is a merge of pre-trained language models created using mergekit.

NOTE: Only tested this just for a bit. YMMV.

Next Day Tests...

Downloaded the GGUF model that someone quantized... And... nope. No.

Do not use model.

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

  • Sao10K/Fimbulvetr-11B-v2

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
    - model: FimbMagic
      layer_range: [0, 13]
  - sources:
    - model: FimbMagic
      layer_range: [8, 13]
  - sources:
    - model: FimbMagic
      layer_range: [12, 36]
  - sources:
    - model: FimbMagic
      layer_range: [12, 36]
  - sources:
    - model: FimbMagic
      layer_range: [36, 48]
  - sources:
    - model: FimbMagic
      layer_range: [36, 48]
merge_method: passthrough
dtype: bfloat16

Additional Notes

Fimbulvetr 11B is still a very good model. This model is for extreme trailblazers who wants to test stuff!

Eval results? Don't bother.

Last one before I sleep: I'm so sorry Sao10K...

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Model size
20B params
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