🌀 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.
📌 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
🔐 Key Features & Architecture
- 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).
- 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.
- Apache 2.0 Compliance: Fully open-source and permissible for both commercial and non-commercial deployment.
💻 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)
🏋️ 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
🌐 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.
📜 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)}}
}