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="Neuron-AI-Labs/Neuron-12x4B-EXP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Neuron-AI-Labs/Neuron-12x4B-EXP")
model = AutoModelForCausalLM.from_pretrained("Neuron-AI-Labs/Neuron-12x4B-EXP", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
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โš–๏ธ Disclaimer

THIS IS AN EXPERIMENT.

  • The creators are NOT responsible if this model does not work, produces garbage output, or crashes your system.
  • We have clearly stated that this is an experimental model with known issues.
  • By downloading and using this model, you accept full responsibility for any outcomes.
  • If it doesn't work, it's not our fault โ€” it's an experiment, and experiments fail sometimes.

๐Ÿ“œ License

Apache 2.0 (Same as the base Qwen models)

๐Ÿ™ Credits

  • Base models: Qwen Team, Neura-Tech-AI
  • Created as a research experiment.

ยฉ 2026 Neura Tech AI. All rights reserved.

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