Qwen3-Neurotic-Experts-248x71M

ItsVictorTube/Qwen3-Neurotic-Experts-248x71M

Qwen3-Neurotic-Experts-248x71M is a machine learning model from ItsVictorTube. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

Qwen3-Neurotic-Experts-248x71M is a Mixture of Experts (MoE) made with the following models using LazyMergekit: Mostafa8Mehrabi/qwen3-71M-c4-final

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All-time downloads

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Parameters

3.6B

7.1 GB on disk

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Hugging Face

Repo makeup

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.safetensors7.1 GB · 100%

At a glance

License: mit
Model type: qwen3_moe
Access: Public
Created: Jan 1, 2026
Updated: Jan 1, 2026
SHA: 1b957901

Base models

Mostafa8Mehrabi/qwen3-71M-c4-final

Model card

From the Hugging Face model README

Qwen3-Neurotic-Experts-248x71M

Qwen3-Neurotic-Experts-248x71M is a Mixture of Experts (MoE) made with the following models using LazyMergekit:

Mostafa8Mehrabi/qwen3-71M-c4-final

🧩 Configuration

base_model: Mostafa8Mehrabi/qwen3-71M-c4-final
gate_mode: random
dtype: float16
experts:
  - source_model: Mostafa8Mehrabi/qwen3-71M-c4-final
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💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "ItsVictorTube/Qwen3-Neurotic-Experts-248x71M"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Type: qwen3_moe
License: mit
Created: Jan 1, 2026
Updated: Jan 1, 2026