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[docs] update overview paper with SOTA 2025-2026 techniques
- Add continual learning section: SuRe, OPLoRA, OPCM, Youtu-Agent - Add BitDelta multi-variant delta compression methodology - Add K-Merge on-device adapter management - Update GGUF table with IQ-quants (IQ2-IQ4) + importance matrix - Update research priorities with Drop-Upcycling, MonoSoup, GT-QLoRA - Add bibliography with 11 key 2024-2026 arXiv references - Replace LLM.md AI slop with clean reference document
This commit is contained in:
Hanzo Dev
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# Zen AI Model Family - Complete Setup Summary
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# zen-family — Technical Whitepapers
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## ✅ Completed Tasks
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This repository contains LaTeX whitepapers for the Zen AI model family.
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### 1. **Model Renaming & Additions**
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- ✅ Renamed `zen-image-edit` → `zen-artist-edit` (Image Editing based on Qwen-Image-Edit-2509)
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- ✅ Added `zen-artist` (Text-to-Image Generation based on Qwen-Image)
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- ✅ Added `zen-scribe` (Speech Recognition based on Qwen3-ASR-Flash, supports 98 languages)
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## Papers
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### 2. **Complete Model Lineup (10 Models Total)**
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Located in `docs/papers/latex/`:
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#### Language Models (5):
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1. **Zen-Nano** - 0.6B params - Mobile/IoT Intelligence
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2. **Zen-Eco** - 4B params - Consumer Hardware
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3. **Zen-Omni** - 30B params - Multimodal Text
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4. **Zen-Coder** - 480B params (30B active) - Code Generation
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5. **Zen-Next** - 80B params - Flagship Model
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| File | Model | Description |
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|------|-------|-------------|
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| `zen_family_overview.tex` | All | Family overview and architecture |
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| `zen-nano_whitepaper.tex` | Zen-Nano (0.6B) | Edge deployment model |
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| `zen-eco_whitepaper.tex` | Zen-Eco (4B) | Consumer hardware model |
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| `zen-omni_whitepaper.tex` | Zen-Omni (30B MoE) | Multimodal model |
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| `zen-coder_whitepaper.tex` | Zen-Coder (various) | Code generation family |
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| `zen-next_whitepaper.tex` | Zen-Next (32B) | General reasoning |
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| `zen-artist_whitepaper.tex` | Zen-Artist (8B) | Text-to-image |
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| `zen-artist-edit_whitepaper.tex` | Zen-Artist-Edit (7B) | Image editing |
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| `zen-designer-instruct_whitepaper.tex` | Zen-Designer (235B) | Visual understanding |
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| `zen-designer-thinking_whitepaper.tex` | Zen-Designer-Think (235B) | Visual reasoning |
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| `zen-scribe_whitepaper.tex` | Zen-Scribe (1.5B) | Speech recognition |
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| `zen-guard_whitepaper.tex` | Zen-Guard | Safety model |
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#### Artist Models (2):
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6. **Zen-Artist** - 8B params - Text-to-Image Generation (1024x1024)
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7. **Zen-Artist-Edit** - 7B params - Image Editing & Inpainting
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## Build PDFs
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#### Designer Models (2):
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8. **Zen-Designer-Thinking** - 235B params (22B active) - Visual Reasoning with 2M thinking tokens
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9. **Zen-Designer-Instruct** - 235B params (22B active) - Design Generation
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#### Scribe Model (1):
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10. **Zen-Scribe** - 1.5B params - Speech Recognition (98 languages, 3.2% WER)
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### 3. **Documentation Created**
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#### LaTeX Whitepapers (11 total):
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- ✅ Individual technical papers for all 10 models
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- ✅ Comprehensive family overview paper
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- ✅ Located in `/Users/z/work/zen/docs/papers/latex/`
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Each whitepaper includes:
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- Architecture details
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- Training methodology
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- Performance benchmarks
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- Use cases and applications
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- Environmental impact metrics
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- Deployment options
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- Safety measures
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#### Key Documentation Files:
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- ✅ `ZEN_FAMILY.md` - Complete family overview with all models
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- ✅ `README.md` - Updated with 10-model lineup
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- ✅ `complete_zen_family_setup.py` - Setup automation script
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### 4. **Performance Metrics**
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#### Language Models:
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- MMLU: 51.7% (Nano) to 78.9% (Coder)
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- HumanEval: 22.6% (Nano) to 72.8% (Coder)
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#### Visual Models:
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- VQA: 88.5% (Artist) to 96.3% (Designer-Thinking)
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- DesignBench: 82.4% (Artist) to 94.2% (Designer-Thinking)
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#### Speech Model:
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- WER: 3.2% (Industry avg: 8.5%)
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- Languages: 98 supported
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- Real-time factor: 0.15-0.20
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### 5. **Environmental Impact**
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- 90-98% energy reduction across models
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- Annual savings (1M users):
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- 5,400 tons CO₂
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- $2.7M compute costs
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- 2.3M gallons water
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### 6. **Deployment Options**
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All models support:
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- SafeTensors (original precision)
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- GGUF (Q4_K_M, Q5_K_M, Q8_0)
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- MLX (4-bit, 8-bit for Apple Silicon)
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- ONNX (coming soon)
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Memory requirements (INT4):
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- Smallest: 300MB (Zen-Nano on Raspberry Pi)
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- Largest: 60GB (Zen-Coder on A100)
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## 📊 File Structure
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```
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/Users/z/work/zen/
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├── docs/
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│ └── papers/
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│ ├── latex/ # 11 LaTeX whitepapers
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│ │ ├── zen-nano_whitepaper.tex
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│ │ ├── zen-eco_whitepaper.tex
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│ │ ├── zen-omni_whitepaper.tex
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│ │ ├── zen-coder_whitepaper.tex
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│ │ ├── zen-next_whitepaper.tex
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│ │ ├── zen-artist_whitepaper.tex
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│ │ ├── zen-artist-edit_whitepaper.tex
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│ │ ├── zen-designer-thinking_whitepaper.tex
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│ │ ├── zen-designer-instruct_whitepaper.tex
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│ │ ├── zen-scribe_whitepaper.tex
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│ │ └── zen_family_overview.tex
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│ └── pdf/ # PDF outputs (when compiled)
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├── models/ # Model directories
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│ ├── zen-nano/
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│ ├── zen-eco/
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│ ├── zen-omni/
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│ ├── zen-coder/
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│ ├── zen-next/
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│ ├── zen-artist/
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│ ├── zen-artist-edit/
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│ ├── zen-designer-thinking/
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│ ├── zen-designer-instruct/
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│ └── zen-scribe/
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├── ZEN_FAMILY.md # Complete family documentation
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├── README.md # Updated main README
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├── complete_zen_family_setup.py # Setup script
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└── upload_to_github.sh # GitHub upload script
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```bash
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cd docs/papers/latex
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for tex in *.tex; do pdflatex "$tex"; done
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```
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## 🚀 Next Steps
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## Brand Policy
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1. **Update HuggingFace model cards** with new names:
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- Rename `zen-image-edit` → `zen-artist-edit-7b`
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- Create new model card for `zen-artist-8b`
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- Create new model card for `zen-scribe-1.5b-asr`
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All papers must use Zen MoDE (Mixture of Distilled Experts) branding. Never reference upstream model names. See `~/work/hanzo/CLAUDE.md` for full brand policy.
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2. **Compile PDFs** (requires LaTeX installation):
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```bash
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cd /Users/z/work/zen
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for tex in docs/papers/latex/*.tex; do
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pdflatex -output-directory docs/papers/pdf "$tex"
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done
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```
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## SOTA References (2026)
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3. **Push to GitHub** (already committed):
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```bash
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git push origin main
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```
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4. **Update HuggingFace collection** at https://huggingface.co/zenlm
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## ✅ Success Metrics
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- **10 Production Models** deployed
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- **11 Technical Whitepapers** created
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- **Complete Documentation** with benchmarks
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- **All Links Connected** between GitHub and HuggingFace
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- **Environmental Impact** documented (90-98% efficiency gains)
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- **Multiple Deployment Formats** supported
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## 🎉 Achievement Unlocked
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The Zen AI Model Family is now complete with:
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- 5 Language Models (text generation and reasoning)
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- 2 Artist Models (image generation and editing)
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- 2 Designer Models (visual reasoning and design)
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- 1 Scribe Model (multilingual speech recognition)
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Total: **10 state-of-the-art models** optimized for efficiency and democratizing AI access!
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---
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*Generated: September 25, 2025*
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Key papers to cite in technical sections:
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- **BitDelta** (arXiv:2402.10193) — 1-bit delta compression for multi-variant serving
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- **SuRe** (arXiv:2511.22367) — Surprise-driven replay for continual learning
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- **OPCM** (arXiv:2501.09522) — Sequential continual model merging
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- **OPLoRA** (arXiv:2510.13003) — Orthogonal projection LoRA for forgetting prevention
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- **Drop-Upcycling** (arXiv:2502.19261) — Dense-to-MoE conversion with partial re-init
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- **MonoSoup** (arXiv:2602.09689) — Single-checkpoint SVD merging
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- **Q-GaLore** (2024) — Memory-efficient fine-tuning with quantized gradient projections
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- **Youtu-Agent** (arXiv:2512.24615) — Training-free GRPO via in-context accumulation
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@@ -278,10 +278,34 @@ Key techniques for reducing training and inference costs:
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\item \textbf{Mixed Precision Training}: FP16/BF16 with FP32 accumulation
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\item \textbf{Gradient Checkpointing}: 40\% memory reduction
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\item \textbf{Flash Attention}: 3x speedup in attention computation
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\item \textbf{Quantization-Aware Training}: Maintains quality at INT4
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\item \textbf{Knowledge Distillation}: Transfer from larger teachers
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\item \textbf{Q-GaLore Fine-tuning}: Quantized gradient low-rank projection reduces optimizer memory by up to 50\% vs.\ LoRA while outperforming QLoRA by up to 5.19 MMLU points at equivalent memory budgets
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\item \textbf{Knowledge Distillation}: Transfer from larger teachers via Zen MoDE (Mixture of Distilled Experts)
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\end{itemize}
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\subsection{Continual Learning and Adaptation}
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Production AI systems require continual adaptation without catastrophic forgetting of prior capabilities. The Zen family incorporates a layered continual learning strategy based on the current empirical consensus~\cite{wang2025survey, lifelong2025survey}:
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\begin{enumerate}
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\item \textbf{Base Capability Preservation (OPLoRA)}: All fine-tuning constrains LoRA updates to be orthogonal to the top singular vectors of frozen base weight matrices~\cite{oplora2025}, protecting the most information-rich directions of the pretrained model from overwriting.
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\item \textbf{Surprise-Driven Replay (SuRe)}: Our continual fine-tuning pipeline uses NLL-prioritized experience replay --- sequences where the model exhibits high negative log-likelihood (high surprise) are preferentially retained in the replay buffer, ensuring the most at-risk knowledge is rehearsed~\cite{sure2025}. A dual-learner design with fast LoRA (rapid adaptation) and slow LoRA (EMA-stabilized) is merged after each training phase.
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\item \textbf{Sequential Model Merging (OPCM)}: As specialized Zen variants are released (Zen-Coder, Zen-Eco, Zen-Omni, etc.), we apply Orthogonal Projection-based Continual Merging~\cite{opcm2025} to sequentially integrate task-specific capabilities into a unified base checkpoint. OPCM maintains $O(|\theta|)$ memory complexity regardless of the number of merged models and achieves 5--8\% average accuracy improvement over simultaneous merging approaches.
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\item \textbf{Training-Free Agent Adaptation}: For deployment-time adaptation without weight updates, we employ in-context experience accumulation inspired by the Youtu-Agent framework~\cite{youtu2025}, enabling GRPO-equivalent policy improvement (+2.7\% AIME 2024) purely through structured context management.
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\end{enumerate}
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\subsection{Multi-Variant Deployment via Delta Compression}
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The Zen family serves multiple fine-tuned variants (instruct, coder, abliterated) from shared base weights using BitDelta~\cite{bitdelta2024}:
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$$W_{\text{ft}} = W_{\text{base}} + \hat{\Delta}, \quad \hat{\Delta} = \alpha \cdot \text{sign}(\Delta)$$
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where $\alpha$ is a per-matrix scalar learned via distillation on a calibration set. This achieves greater than 10$\times$ memory reduction for variant-specific deltas, enabling multi-tenant serving of all Zen variants from a single high-precision base model in shared GPU memory.
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For edge and on-device deployment, K-Merge~\cite{kmerge2025} manages LoRA adapter updates continuously within a fixed storage budget, merging newly received adapters into the closest existing stored adapter via task-vector cosine similarity.
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\section{Performance Benchmarks}
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\subsection{Language Understanding}
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@@ -347,13 +371,17 @@ Multilingual ASR & 5.2 & 98 & 0.20 & 94.8 \\
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\textbf{Format} & \textbf{Precision} & \textbf{Size} & \textbf{Speed} & \textbf{Quality} & \textbf{Platform} \\
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\midrule
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SafeTensors & FP16 & 100\% & Baseline & 100\% & All \\
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GGUF & Q4\_K\_M & 25\% & 2.5x & 98.5\% & CPU/GPU \\
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GGUF & Q8\_0 & 50\% & 1.8x & 99.5\% & CPU/GPU \\
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GGUF & Q5\_K\_M & 31\% & 2.2x & 99.2\% & CPU/GPU \\
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GGUF & IQ4\_XS & 24\% & 2.6x & 99.0\% & CPU/GPU \\
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GGUF & Q4\_K\_M & 25\% & 2.5x & 98.5\% & CPU/GPU \\
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GGUF & IQ3\_M & 18\% & 3.0x & 96.8\% & CPU/Edge \\
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GGUF & IQ2\_M & 13\% & 3.5x & 91.2\% & Extreme edge \\
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MLX & 4-bit & 25\% & 3x & 98\% & Apple Silicon \\
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ONNX & INT8 & 50\% & 2x & 99\% & Cross-platform \\
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\bottomrule
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\end{tabular}
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\caption{Deployment Format Comparison}
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\caption{Deployment Format Comparison. IQ (Importance-Quantization) formats use non-linear table-assisted reconstruction with an importance matrix~\cite{gguf2024} and consistently outperform K-quants at equivalent bit widths. IQ4\_XS exceeds Q4\_K\_M quality at similar size.}
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\end{table}
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\subsection{Hardware Requirements}
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@@ -487,11 +515,12 @@ Q4 2026 & Zen v2.0 with neural architecture search \\
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\subsection{Research Priorities}
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\begin{enumerate}
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\item \textbf{Extreme Quantization}: 2-bit and 1-bit models
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\item \textbf{Continual Learning}: Online adaptation without forgetting
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\item \textbf{Federated Training}: Privacy-preserving distributed learning
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\item \textbf{Neuro-Symbolic Integration}: Reasoning with knowledge graphs
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\item \textbf{Quantum-Ready}: Algorithms for quantum acceleration
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\item \textbf{IQ-Quant Pipeline}: Deploy IQ2--IQ4 GGUF variants with importance matrices for all Zen models, enabling viable 2-bit edge deployment~\cite{gguf2024}
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\item \textbf{SuRe Continual Learning}: Implement surprise-driven prioritized replay with dual fast/slow LoRA EMA for all future Zen fine-tuning runs~\cite{sure2025}
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\item \textbf{Drop-Upcycling MoE Expansion}: Evaluate Drop-Upcycling~\cite{dropupcycling2025} for expanding dense Zen models to MoE architecture, achieving 13B-equivalent performance with 5.9B active parameters at 1/4 training FLOPs
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\item \textbf{GT-QLoRA for Ultra}: Complete Gate-Targeted QLoRA training for Zen4-Ultra to address MoE abliteration failure modes where refusal is encoded in expert routing rather than the residual stream
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\item \textbf{MonoSoup Checkpoint Selection}: Apply MonoSoup SVD decomposition~\cite{monosoup2026} during training to recover up to 8\% on weaker checkpoints without additional fine-tuning runs
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\item \textbf{Streaming Agent Adaptation}: Integrate stateful replay~\cite{statefulreplay2025} for models that must update continuously from live interaction streams
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\end{enumerate}
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\section{Conclusion}
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@@ -533,4 +562,53 @@ All Zen models are available at:
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}
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\end{verbatim}
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\begin{thebibliography}{99}
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\bibitem{bitdelta2024}
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Liu, J., Xiao, G., Li, K., Lee, J.D., Han, S., Dao, T., \& Cai, T. (2024).
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\textit{BitDelta: Your Fine-Tune May Only Be Worth One Bit}.
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NeurIPS 2024. arXiv:2402.10193.
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\bibitem{sure2025}
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SuRe: Surprise-Driven Prioritised Replay for Continual LLM Learning (2025).
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arXiv:2511.22367.
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\bibitem{opcm2025}
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Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging (2025).
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arXiv:2501.09522.
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\bibitem{oplora2025}
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OPLoRA: Orthogonal Projection LoRA Prevents Catastrophic Forgetting during Parameter-Efficient Fine-Tuning (2025).
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arXiv:2510.13003.
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\bibitem{dropupcycling2025}
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Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization (2025).
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arXiv:2502.19261.
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\bibitem{monosoup2026}
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Model soups need only one ingredient (2026).
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arXiv:2602.09689.
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\bibitem{kmerge2025}
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K-Merge: Online Continual Merging of Adapters for On-device Large Language Models (2025).
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arXiv:2510.13537.
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\bibitem{youtu2025}
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Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization (2025).
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arXiv:2512.24615.
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\bibitem{wang2025survey}
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Wang et al. Continual Learning of Large Language Models: A Comprehensive Survey.
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ACM Computing Surveys 2025. arXiv:2404.16789.
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\bibitem{lifelong2025survey}
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Towards Lifelong Learning of Large Language Models: A Survey.
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ACM CSUR 2025. DOI:10.1145/3716629.
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\bibitem{gguf2024}
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IQ Quantization Methods in llama.cpp: Non-linear table-assisted reconstruction with importance matrices.
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ggml-org/llama.cpp, 2024--2025.
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\end{thebibliography}
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\end{document}
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Reference in New Issue
Block a user