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="Infermatic/MN-12B-Inferor-v0.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Infermatic/MN-12B-Inferor-v0.0")
model = AutoModelForCausalLM.from_pretrained("Infermatic/MN-12B-Inferor-v0.0")
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

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Inferor

My first merge yay!

This was made thanks to infermatic.ai

Recommended settings on: Infermatic/MN 12B Inferor v0.0 Article

Thanks everyone that is using it and providing feedback. ily - svak

Our discord server is open for discussions for this model.

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 anthracite-org/magnum-v4-12b 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: anthracite-org/magnum-v4-12b
dtype: bfloat16
merge_method: model_stock
slices:
- sources:
  - layer_range: [0, 40]
    model: Fizzarolli/MN-12b-Sunrose
  - layer_range: [0, 40]
    model: nbeerbower/Mistral-Nemo-Gutenberg-Doppel-12B-v2
  - layer_range: [0, 40]
    model: nothingiisreal/MN-12B-Starcannon-v3
  - layer_range: [0, 40]
    model: anthracite-org/magnum-v4-12b
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