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="Vortex5/Moonlit-Shadow-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vortex5/Moonlit-Shadow-12B")
model = AutoModelForCausalLM.from_pretrained("Vortex5/Moonlit-Shadow-12B")
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]:]))
Quick Links

merge

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

Merge Details

Merge Method

This model was merged using the Model Stock merge method using mistralai/Mistral-Nemo-Instruct-2407 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:


base_model: mistralai/Mistral-Nemo-Instruct-2407
models:
  - model: LatitudeGames/Muse-12B
  - model: anthracite-org/magnum-v4-12b
  - model: yamatazen/NeonMaid-12B-v2
  - model: SicariusSicariiStuff/Impish_Nemo_12B
  - model: crestf411/MN-Slush
  - model: Epiculous/Violet_Twilight-v0.2
  - model: LatitudeGames/Wayfarer-12B
  - model: inflatebot/MN-12B-Mag-Mell-R1
  - model: nothingiisreal/MN-12B-Celeste-V1.9
  - model: Nitral-AI/Captain-Eris_Violet-V0.420-12B
  - model: PocketDoc/Dans-SakuraKaze-V1.0.0-12b 
merge_method: model_stock
dtype: bfloat16
parameters:
  normalize: true
tokenizer:
  source: union
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