# M1llion-35B
> **Flagship Model of m1llionAI | Built & Maintained by ArcOffical**  
> *Practical, Efficient, Privacy-First 35B Parameter MoE LLM — Deployable on Consumer Hardware (<10GB)*

[![Hugging Face Model](https://img.shields.io/badge/Hugging%20Face-m1llionAI/M1llion-35B-blue)](https://huggingface.co/m1llionAI/M1llion-35B)
[![GitHub Repository](https://img.shields.io/badge/GitHub-M1llion-AI/million-35b-lightgrey)](https://github.com/M1llion-AI/million-35b)
[![License: Research Only](https://img.shields.io/badge/License-Research%20Only-red)](#license)

## 🚀 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
```bash
# 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
```python
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
```python
# 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
```python
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):
```bash
# 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:
```bash
# 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:
```bash
# 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):
```bash
# 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](https://huggingface.co/m1llionAI/M1llion-35B/discussions)
- **m1llionAI Organization**: [https://huggingface.co/m1llionAI](https://huggingface.co/m1llionAI)
- **GitHub Repository**: [https://github.com/M1llion-AI/million-35b](https://github.com/ArcOffical/million-35b)

---

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