M1llion-35B

Flagship Model of m1llionAI | Built & Maintained by ArcOffical
Practical, Efficient, Privacy-First 35B Parameter MoE LLM — Deployable on Consumer Hardware (<10GB)

🚀 Quick Overview

M1llion-35B is a state-of-the-art 35 billion parameter Mixture-of-Experts (MoE) multimodal large language model designed and built exclusively by ArcOffical, under the m1llionAI Hugging Face organization. It redefines accessible high-performance AI by balancing enterprise-grade capabilities with edge-deployable efficiency—all while prioritizing user privacy and data security.

Unlike traditional 35B+ parameter models that require cloud infrastructure or high-end GPUs, M1llion-35B can be deployed on consumer hardware (<10GB storage via QEPQ compression) with minimal performance loss (<0.1%) and a industry-leading low hallucination rate (<1.2%).

Key Model Specifications at a Glance

Specification	Details
Total Parameters	~35 Billion (multimodal MoE)
Active Parameters	~7 Billion (per-token inference)
Deployment Size	<10 GB (QEPQ Quantum-Entangled Compression)
Context Window	8192 tokens
Vocabulary Size	256,000 (multilingual)
Hallucination Rate	<1.2% (Reality Anchoring Technology)
Framework Support	TensorFlow 2.x / PyTorch 2.x
Deployment Type	Local/Edge (no cloud dependency)
Security Architecture	Hundreds Security Architecture (HSA)
Multimodal Support	Text, Image, Video, Audio + Screen Recognition

🌟 Key Highlights

1. Extreme Edge Efficiency: 7x compression ratio via QEPQ technology, enabling <10GB deployment on consumer laptops/desktops—no cloud or high-end GPU required.
2. Privacy-First by Design: Runs entirely on local devices; no user data is transmitted to servers, and all memory/habit learning is stored and processed offline.
3. Low Hallucination & High Reliability: Powered by Reality Anchoring, achieving <1.2% hallucination rate for factual reasoning, making it suitable for technical and decision-critical tasks.
4. Full-Stack Multimodal Agent: Integrates VisionPerceptionModule (VPM) for screen recognition, autonomous UI actions (clicks, scrolls), and emotion-aware dialogue.
5. Top-Tier Security: Built-in Hundreds Security Architecture (HSA) to mitigate prompt injection, model tampering, and data leaks during inference.
6. Open-Source & Customizable: Dual-framework support, full pre-training/finetuning pipelines, and open-source compression tools for developer customization.

👤 Creator & Maintainer

ArcOffical is the sole founding author, lead developer, and core maintainer of M1llion-35B. With deep expertise in MoE architecture design, extreme model compression, and multimodal agent development, ArcOffical led the entire lifecycle of this model—from initial prototyping and curriculum pre-training to proprietary technology integration and open-source deployment.

This model is a flagship project of m1llionAI (a Hugging Face organization dedicated to accessible, privacy-first edge AI), where ArcOffical drives the mission to democratize cutting-edge LLM technology for all users.

🚦 Quick Start (Hugging Face Transformers)

Get up and running with M1llion-35B in minutes using the Hugging Face transformers library.

Prerequisites

# Install required dependencies
pip install transformers>=4.36.0 torch>=2.0.0 accelerate>=0.25.0 pillow>=10.0.0

1. Load the Model & Tokenizer

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load pre-trained model and tokenizer from Hugging Face Hub
model_name = "m1llionAI/M1llion-35B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",  # Automatically assign layers to available hardware
    load_in_8bit=True,  # Enable 8-bit inference for edge efficiency (optional)
    trust_remote_code=True  # Required for custom MoE and VPM modules
)

2. Text Inference Example

# Sample prompt (supports conversational and instruction-based inputs)
prompt = """
You are a helpful, privacy-first AI assistant running on local hardware. 
Explain the key benefits of M1llion-35B in simple terms.
"""

# Tokenize input
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate output (configure parameters for efficiency and quality)
outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.7,
    top_p=0.95,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

# Decode and print result
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("M1llion-35B Response:\n", response)

3. Multimodal (Image + Text) Inference Example

from PIL import Image

# Load sample image (screen capture, photo, or document)
image_path = "sample_screen.png"
image = Image.open(image_path).convert("RGB")

# Multimodal prompt (ask the model to analyze the screen image)
multimodal_prompt = """
Analyze the attached screen image and list the key UI elements you can identify. 
Suggest one simple action to complete the most obvious task on the screen.
"""

# Tokenize text and process image (custom multimodal pipeline)
multimodal_inputs = tokenizer(
    multimodal_prompt,
    images=image,  # Custom parameter for VPM integration
    return_tensors="pt"
).to(model.device)

# Generate multimodal response
multimodal_outputs = model.generate(
    **multimodal_inputs,
    max_new_tokens=300,
    temperature=0.6,
    top_p=0.9
)

# Decode and print result
multimodal_response = tokenizer.decode(multimodal_outputs[0], skip_special_tokens=True)
print("M1llion-35B Multimodal Response:\n", multimodal_response)

📊 Model Details

Architecture

M1llion-35B adopts a decoder-only MoE Transformer architecture with the following core components:

• 32 Transformer layers with 4096 hidden dimension
• 8 total experts (2 activated per token) for sparse efficiency
• Grouped-Query Attention (32 heads) for memory-efficient long-context modeling
• Rotary Positional Embeddings (RoPE) for 8k+ token context support
• Custom VisionPerceptionModule (VPM) for cross-modal fusion

Pre-Training

• Curriculum: 4-stage multi-modal pre-training (foundation knowledge → context extension → advanced reasoning → high-quality annealing)
• Token Count: 15 trillion total tokens (multilingual text, code, mathematics, visual data)
• Data Sources: mOSCAR, Maya-LLaVA-Pretrain, OpenAssistant/oasst1, and curated screen UI datasets

Fine-Tuning

• Supervised Fine-Tuning (SFT): 3-stage text + 4-stage multimodal fine-tuning for human alignment
• Reinforcement Learning (RL): RLHF for harmlessness/usefulness + agent RL for autonomous action capability
• Privacy-Preserving Fine-Tuning (PPFT): Support for on-device custom fine-tuning without data leakage

Compression Technology (QEPQ)

M1llion-35B's extreme compression is powered by QEPQ (Quantum-Entangled Pruning & Quantization):

• 2-bit nonlinear codebook quantization for weight compression
• 60% pruning of non-critical weights based on quantum entanglement metrics
• Gzip secondary compression for additional storage savings
• <0.1% performance loss compared to full FP16 model

📈 Benchmark Results

M1llion-35B achieves competitive performance across text, multimodal, and agent benchmarks—while maintaining edge-deployable efficiency.

Key Performance Highlights

Benchmark Category	Metrics (M1llion-35B)
English Text Reasoning	MMLU: 87.7, PIQA: 76.7, GSM8K: 89.2, MT-Bench: 8.6/10
Korean Text Reasoning	KMMLU: 71.3, HAERAE Bench 1.0: 87.4, KoBALT: 50.6
Multimodal (Vision-Text)	KoNET: 75.1, K-MMBench: 88.1, TextVQA: 85.4
Intelligent Agent	Tau2-Airline: 58.0, Tau2-Retail: 71.6, Terminal Bench: 21.8
Efficiency	Inference Latency (8k tokens): 150ms (consumer GPU), 450ms (consumer CPU)

Deployment Efficiency Comparison

Configuration	Model Size	Performance Loss	Supported Hardware
FP16 (Baseline)	~70 GB	0.0%	High-end enterprise GPU
FP8 (Traditional)	~35 GB	0.5%	Mid-range GPU
QEPQ Compression (2-bit)	<10 GB	<0.1%	Consumer GPU/CPU/laptops

🛠️ Advanced Usage Guides

1. Local Model Training

Use the official training script to fine-tune M1llion-35B on custom datasets (on-device, no cloud):

# Fine-tune M1llion-35B on custom instruction data (test mode first)
python train.py \
  --model_path ./local/m1llion-35b \
  --dataset_path ./custom_datasets/instruction_data.json \
  --output_dir ./fine_tuned_model \
  --num_steps 5000 \
  --batch_size 2 \
  --gradient_accumulation_steps 16 \
  --test_mode

2. QEPQ Model Compression

Compress the full model to edge-ready <10GB size using the official compression toolkit:

# Compress full M1llion-35B model to edge-ready format
python compress.py \
  --mode compress \
  --model_path ./full_m1llion_35b \
  --output_path ./m1llion_35b_edge \
  --compression_level qepq_2bit \
  --preserve_multimodal

3. Run Benchmark Evaluations

Generate a detailed benchmark report for custom model variants:

# Evaluate fine-tuned/compressed model against industry benchmarks
python run_evaluation.py \
  --model_path ./m1llion_35b_edge \
  --benchmarks mmlu,gsm8k,mt_bench \
  --output_report ./benchmark_results.md

4. Edge Deployment (Consumer Laptop/CPU)

Deploy the compressed M1llion-35B model on a consumer laptop (no GPU required):

# Load edge model and run local inference server
python deploy_edge.py \
  --compressed_model_path ./m1llion_35b_edge \
  --port 8080 \
  --device cpu \
  --enable_multimodal

⚙️ Configuration

Core model parameters can be customized via the m1_blueprint.json configuration file (included in the GitHub repository), including:

• MoE expert count and routing parameters
• QEPQ compression level
• HSA security settings (threat detection thresholds)
• Multimodal VPM resolution and processing limits
• Training/finetuning hyperparameters

❓ FAQs

1. Q: Can I deploy M1llion-35B on my personal laptop?
   A: Yes! The QEPQ-compressed variant (<10GB) runs on most modern laptops (8GB+ RAM, 4-core+ CPU, or integrated GPU).

2. Q: Is M1llion-35B suitable for commercial use?
   A: No. This model is for research and non-commercial use only. Commercial authorization requires direct contact with ArcOffical/m1llionAI.

3. Q: What are the "surprise hidden features" mentioned in the launch announcement?
   A: Hidden features (unveiled on February 14, 2026) include cross-device local AI synchronization and advanced SWE agent capabilities—stay tuned to the m1llionAI Hugging Face organization for updates.

4. Q: How do I report bugs or request features?
   A: Submit issues via the m1llionAI company in hugging face or comment on the M1llion-35B Hugging Face model page (monitored by ArcOffical).

🤝 Contribution

m1llionAI and ArcOffical welcome community contributions to M1llion-35B! To contribute:

1. Fork the m1llion ai company organization for hiring
2. Submit a Pull Request with detailed descriptions of your changes (model optimization, benchmarking, bug fixes, etc.)
3. Adhere to the project's code style and privacy-first design principles

All contributions will be reviewed by ArcOffical and integrated into the main model branch if aligned with the project's mission.

📄 License

M1llion-35B is licensed for non-commercial research and learning use only. Commercial use, redistribution, or modification for commercial purposes is prohibited without prior written authorization from ArcOffical and m1llionAI.

🙏 Acknowledgments

• ArcOffical for the full design, development, and maintenance of M1llion-35B
• Collaboration teams (pure-team, cogent-ai, Arc4, neo-ai-team) for technical insights and dataset curation
• Hugging Face for providing the open-source ecosystem to democratize AI access
• The broader LLM community for advances in MoE architecture, compression, and multimodal AI

📧 Contact

• Core Maintainer (ArcOffical): Accessible via the M1llion-35B Hugging Face Model Discussions
• m1llionAI Organization: https://huggingface.co/m1llionAI
• GitHub Repository: https://github.com/M1llion-AI/million-35b

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Release Date: February 14, 2026 (UTC+8)
Last Updated: January 9, 2026
Built by ArcOffical | m1llionAI | Privacy-First, Edge-Ready, Future-Proof AI