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="OccultAI/Morpheus-8B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OccultAI/Morpheus-8B-v1")
model = AutoModelForCausalLM.from_pretrained("OccultAI/Morpheus-8B-v1", 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]:]))
Quick Links

⚠️ Warning: This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly, and use Llama 3 chat template.

Morpheus 8B v1

Recommended Settings: Temp 1.0, TopNSigma 1.25

Morpheus

{'loss': 0.5651, 'grad_norm': 3.787045478820801, 'learning_rate': 1.0386570913148586e-05, 'entropy': 0.7085917145013809, 'num_tokens': 588832.0, 'mean_token_accuracy': 0.8508399426937103, 'epoch': 4.0}

Model Q0 Score Quant Q0G Refusals
Morpheus 8B v1 15365 Q6_K Pass 0/100
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Model size
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