# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("head-empty-ai/Codename-Alpha-Test")
model = AutoModelForCausalLM.from_pretrained("head-empty-ai/Codename-Alpha-Test")Quick Links
Codename-Alpha-Test
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: NeverSleep/X-NoroChronos-13B
layer_range: [0, 8]
- sources:
- model: Undi95/MLewdBoros-L2-13B
layer_range: [8, 16]
- sources:
- model: NeverSleep/X-NoroChronos-13B
layer_range: [16, 24]
- sources:
- model: Undi95/MLewdBoros-L2-13B
layer_range: [24, 32]
- sources:
- model: NeverSleep/X-NoroChronos-13B
layer_range: [32, 40]
merge_method: passthrough
dtype: float16
- Downloads last month
- 5
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="head-empty-ai/Codename-Alpha-Test")