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="Naphula/Wicked-Moondust-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Naphula/Wicked-Moondust-12B")
model = AutoModelForCausalLM.from_pretrained("Naphula/Wicked-Moondust-12B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
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🌙 Wicked Moondust 12B

Wicked-Moondust-12B

This is a merge of pre-trained language models created through a multi-stage combination using mergekit.

Merge Details

Merge Method

This model was merged using the Arcee Fusion merge method.

Models Merged

The following models were included in the merge:

  • Vortex5/Ethereal-Stardust-12B
  • Vortex5/Wicked-Oblivion-12B
  • Vortex5/Nether-Moon-12B

Configuration

The following YAML configuration was used to produce this model:

name: First
architecture: MistralForCausalLM
base_model: B:\12B\Vortex5--Ethereal-Stardust-12B
models:
  - model: B:\12B\Vortex5--Ethereal-Stardust-12B
  - model: B:\12B\Vortex5--Wicked-Oblivion-12B
merge_method: arcee_fusion
parameters:  
  tukey_fence: 1.5
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: B:\12B\Vortex5--Ethereal-Stardust-12B
chat_template: auto
---
name: Wicked Moondust 12B
architecture: MistralForCausalLM
base_model: First
models:
  - model: First
  - model: B:\12B\Vortex5--Nether-Moon-12B
merge_method: arcee_fusion
parameters:  
  tukey_fence: 1.5
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: First
chat_template: auto
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