🌀 Muon AI — PULSE-1B

PULSE-1B (Part of the Muon AI ecosystem) is a lightweight, high-efficiency language model fine-tuned from Qwen/Qwen2.5-0.5B-Instruct. Designed specifically for privacy-first, edge-assisted, and encrypted web application workflows, it offers strong instruction-following capabilities while keeping resource requirements minimal.

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📌 Model Overview

• Developed by: Muon AI
• Model Type: Causal Language Model (Transformer)
• Base Model: Qwen/Qwen2.5-0.5B-Instruct
• Parameters: ~494M (0.5B)
• License: Apache 2.0
• Fine-Tuning Method: LoRA (Low-Rank Adaptation) merged with base weights
• Primary Use Case: Private AI Chat Assistant, Encrypted Web App Backends, Edge & Local Inferences

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🔐 Key Features & Architecture

1. Privacy-First Design: Optimized to run behind end-to-end encrypted API bridges (e.g., AES-GCM encrypted Gradio endpoints for the Muon AI Web Interface).
2. Ultra-Lightweight Footprint: Requires minimal GPU VRAM (< 2 GB in FP16/BF16) or CPU memory, making it highly cost-effective to host on free or low-tier infrastructure like Hugging Face Spaces.
3. Apache 2.0 Compliance: Fully open-source and permissible for both commercial and non-commercial deployment.

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💻 How to Use

1. Direct Inference with Hugging Face transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "muonai/PULSE-1B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto"
)

# Chat-formatted input
messages = [
    {"role": "system", "content": "You are PULSE-1B, a secure and private AI assistant developed by Muon AI."},
    {"role": "user", "content": "Explain quantum computing in three simple sentences."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

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🏋️ Training & Fine-Tuning Details

• Dataset: Fine-tuned on the Salesforce/wikitext (wikitext-2-raw-v1) dataset to refine text structure, density, and general reasoning.

• Hardware: Trained on Google Colab (NVIDIA T4 GPU).

• LoRA Configuration:

• Rank ($r$): 8

• Alpha ($\alpha$): 16

• Target Modules: q_proj, v_proj

• Dropout: 0.05

• Optimizer: AdamW (paged 8-bit)

• Epochs: 1

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🌐 Integration with Muon Web Application

PULSE-1B is designed to power the Muon AI Private Web App:

• Zero Local Footprint Leakage: Chats are encrypted on the user's client side before transmission.
• Client Storage: Conversations are stored locally in the user's browser localStorage.
• Stateless Backend: The model receives encrypted payloads, decrypts in memory, generates text, encrypts the output, and returns it to the client.

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📜 Citation & License

PULSE-1B is released under the Apache 2.0 License, inheriting the open-weights permissions of the base model Qwen2.5.

@misc{muon_pulse_1b_2026,
  author = {Muon AI},
  title = {PULSE-1B: A Lightweight, Privacy-First Language Model},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{[https://huggingface.co/muonai/PULSE-1B](https://huggingface.co/muonai/PULSE-1B)}}
}