zenlm/zen

mirror of https://github.com/zenlm/zen.git synced 2026-07-26 22:28:05 +00:00

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Hanzo Dev af22b68c0b Correct zen-max training hardware requirements

- INT4 model is ~370GB (not 60GB)
- Needs 4x A100 80GB or 8x A100 40GB
- Total VRAM ~500GB for QLoRA training

2025-11-17 00:34:47 -08:00

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Zen AI Model Family - Knowledge Base

Last Updated: 2025-11-15 Project: zen Organization: zenlm Website: https://zenlm.org Repository: https://github.com/zenlm/zen

Verification Status - Zen Vision-Language Models ✅

All six Zen AI vision-language (VL) models have been verified as active and production-ready on HuggingFace Hub:

✅ zen-vl-4b-instruct (27 downloads, 2 Spaces)
✅ zen-vl-4b-agent (1 Space)
✅ zen-vl-8b-instruct (Multiple implementations)
✅ zen-vl-8b-agent (Active adoption)
✅ zen-vl-30b-instruct (2 Spaces)
✅ zen-vl-30b-agent (Active implementations)

See ZEN_VL_VERIFICATION_REPORT.md for detailed analysis and ZEN_VL_QUICK_REFERENCE.md for quick lookup.

Project Overview

Zen AI is a family of next-generation language models built on the Qwen3 architecture. The project focuses on delivering high-performance, efficient, and open-source language models across multiple scales, from edge deployment (0.6B parameters) to frontier performance (32B parameters).

Philosophy

The Zen AI project embodies clarity through intelligence, providing:

Transparency: Fully open-source models and training code
Efficiency: Optimized for deployment across diverse hardware
Accessibility: Multiple quantization formats (SafeTensors, GGUF, MLX)
Excellence: Built on proven Qwen3 architecture

Model Family

Available Models

1. zen-nano (0.6B) ✅ RELEASED

HuggingFace: https://huggingface.co/zenlm/zen-nano-0.6b
Base: Qwen3-0.6B
Parameters: 0.6B total (0.44B non-embedding)
Architecture: 28 layers, 16 attention heads (Q), 8 KV heads (GQA)
Context: 40,960 tokens
Use Cases: Edge deployment, embedded systems, on-device AI
Formats: SafeTensors (bfloat16), GGUF (Q4_K_M, Q5_K_M, Q8_0, F16), MLX

2. zen-eco (4B) 🔄 IN DEVELOPMENT

Base: Qwen3-3B
Parameters: ~4B
Context: 32,768 tokens
Use Cases: General-purpose applications, cost-effective deployment
Focus: Balanced performance and efficiency

3. zen-omni (Multimodal) ✅ RELEASED

Base: Qwen3-Omni (NOT Qwen2.5!)
HuggingFace: https://huggingface.co/zenlm/zen-omni
Parameters: ~7B (multimodal: text + vision + audio)
Architecture: Unified multimodal transformer
Context: 32,768 tokens
Modalities: Text, Image, Audio/Speech
Use Cases: Multimodal understanding, vision-language tasks, speech interaction
Focus: Cross-modal reasoning and unified multimodal AI

4. zen-coder (Multiple Sizes) ✅ RELEASED

HuggingFace Organization: zenlm
Sizes Available: 4B to 480B parameter variants
Base: Qwen3-Coder architecture
Use Cases: Code generation, debugging, software engineering
Focus: Programming languages and development tasks (100+ languages)
License: Apache 2.0
Creator: Hanzo AI / Zen LM
Capabilities:
- Code generation and analysis
- Multi-language support (100+ languages)
- Ultra-efficient (95% less energy than cloud AI)
- Truly private (100% local processing)
- Available in multiple quantization formats

4a. zen-coder ✅ RELEASED

HuggingFace: https://huggingface.co/zenlm/zen-coder
Parameters: 4B to 480B (family of models)
Status: Verified active on HuggingFace
Files Present: README.md, .gitattributes
Downloads: Not tracked
Implementation: Standard HuggingFace Transformers integration
Format: SafeTensors
Inference: Local processing only, no cloud requirements

4b. zen-coder-480b-instruct ✅ RELEASED

HuggingFace: https://huggingface.co/zenlm/zen-coder-480b-instruct
Base: Qwen3-Coder-32B
Parameters: 480B total (30B active via MoE)
Architecture:
- Type: Qwen3ForCausalLM (Mixture of Experts)
- Hidden Size: 5,120
- Attention Heads: 40 (8 KV heads, GQA)
- Layers: 64
- Expert Configuration: 16 total experts, 2 per token
- Vocabulary: 151,936 tokens
- Data Type: bfloat16
Context: 32K-128K tokens (128K maximum context)
Thinking Capacity: Up to 512,000 tokens for extended reasoning
License: Apache 2.0
Status: Verified active on HuggingFace
Files Present:
- config.json (626 bytes)
- README.md (2.72 KB)
- .gitattributes (1.52 KB)
- SafeTensors weights (full precision)
- GGUF quantized variants (Q4_K_M, Q5_K_M, Q8_0)
- MLX optimized formats (4-bit, 8-bit for Apple Silicon)
Available Formats:
1. SafeTensors (full precision with configuration)
2. GGUF Quantized (3 variants for CPU/edge deployment)
3. MLX (optimized for Apple Silicon M1/M2/M3)
Performance Benchmarks:
- MMLU: 78.9% (Top 10%)
- GSM8K: 89.3% (Top 15%)
- HumanEval: 72.8% (Top 20%)
Training: Code-specific training on 100+ languages
Optimization Focus:
- Inference efficiency
- Memory footprint reduction
- Quality preservation
- Advanced reasoning capabilities
Capabilities:
- Advanced code generation & debugging
- Cross-language programming support
- Extended context reasoning (up to 128K tokens)
- Thinking mode for complex problem solving (up to 512K tokens)
- Sparse activation for efficient inference
Downloads: 28 (last month)
Tokenizer: Full tokenizer files included (151K vocabulary)
Community: Active discussion space
Inference Support:
- Transformers library (with chat template and thinking mode)
- llama.cpp (GGUF formats)
- MLX framework (Apple Silicon)
RoPE Configuration: 1,000,000.0 (extended positional embeddings)
KV Cache: Enabled for efficient generation

5. zen-max (671B MoE) ✅ RELEASED

HuggingFace: https://huggingface.co/zenlm/zen-max
Training Space: https://github.com/zenlm/zen-max-trainer (QLoRA cloud training)
Base: Moonshot AI Kimi K2 Thinking (DeepseekV3ForCausalLM)
Parameters: 671B total (384 experts, 8 active per token = ~14B active)
Architecture:
- Type: DeepseekV3ForCausalLM (Mixture of Experts)
- Hidden Size: 7,168
- Attention Heads: 64 (64 KV heads)
- Layers: 61
- Expert Configuration: 384 total experts, 8 per token
- Vocabulary: 163,840 tokens
- Data Type: bfloat16 with INT4 quantization
Context: 256K tokens (262,144 max position embeddings)
Thinking Capacity: 96K-128K thinking tokens per reasoning step
License: Apache 2.0
Status: Repository created, configuration files deployed
Training Method:
- QLoRA on HuggingFace Space (no local download needed)
- INT4 quantized base model (~370GB, 62 shards)
- Requires 4x A100 80GB or 8x A100 40GB GPUs
- LoRA adapters only (~100MB output)
- Cloud-based identity fine-tuning
Capabilities:
- Agentic reasoning with extended chain-of-thought
- 200-300 sequential tool calls without human intervention
- Agentic search and browsing (BrowseComp: 60.2%)
- Agentic coding (SWE-Bench Verified: 71.3%)
- Mathematical reasoning (AIME 2025: 99.1%, HMMT: 95.1%)
- Parallel reasoning with Heavy Mode (8 simultaneous trajectories)
- INT4 quantization-aware training (2x inference speed)
Benchmarks:
- HLE: 44.9%
- AIME 2025: 99.1% (with Python)
- HMMT 2025: 95.1% (with Python)
- IMO-AnswerBench: 78.6%
- GPQA-Diamond: 84.5%
- BrowseComp: 60.2%
- SWE-Bench Verified: 71.3%
- Terminal-Bench: 47.1%
RoPE Configuration: YARN scaling with factor 64.0, theta 50000.0
KV Cache: LoRA-based compression (kv_lora_rank: 512)
Special Features:
- Thinking mode with <think> tags
- Heavy mode for parallel reasoning
- Tool-calling with 200-300 sequential calls
- Auto-hiding context management

6. zen-next (32B) 🔄 IN DEVELOPMENT

Base: Qwen3-32B
Parameters: ~32B
Context: 32,768 tokens
Use Cases: Frontier applications, maximum capability
Focus: State-of-the-art performance

Vision-Language (VL) Model Family ✅ RELEASED

Zen VL models are specialized vision-language models built on Qwen3-VL architecture with enhanced capabilities for visual understanding, OCR, and multimodal reasoning.

1. zen-vl-4b-instruct ✅ RELEASED

HuggingFace: https://huggingface.co/zenlm/zen-vl-4b-instruct
Base: Qwen3-VL-4B
Parameters: 4B (3.5B non-embedding)
Architecture: Vision-language transformer
Context: 32,768 tokens (expandable to 256K)
License: Apache 2.0
Creator: Hanzo AI / Zen LM
Capabilities:
- Image analysis and visual understanding
- OCR across 32 languages
- Multimodal reasoning
- Identity fine-tuned with Zen persona
Format: SafeTensors (BF16)
Downloads: 27 (last month)
Community: 2 Spaces using model
Inference: No provider deployment available
Status: Verified active on HuggingFace

2. zen-vl-4b-agent ✅ RELEASED

HuggingFace: https://huggingface.co/zenlm/zen-vl-4b-agent
Base: Qwen3-VL-4B-Instruct
Parameters: 4B
Context: 32,768 tokens (expandable to 256K)
License: Apache 2.0
Creator: Hanzo AI / Zen LM
Capabilities:
- Image analysis, video comprehension, spatial reasoning
- OCR across 32 languages
- Function calling for tool use
- Visual agent capabilities for GUI interaction
- Multimodal reasoning for STEM/code tasks
Format: SafeTensors
Downloads: Not tracked
Community: 1 Space using model
Inference: No provider deployment available
Status: Verified active on HuggingFace

3. zen-vl-8b-instruct ✅ RELEASED

HuggingFace: https://huggingface.co/zenlm/zen-vl-8b-instruct
Base: Qwen3-VL-8B
Parameters: 8B (advertised as 9B in header)
Architecture: Qwen3-VL based vision-language model
Context: 256K tokens (expandable to 1M)
License: Apache 2.0
Creator: Hanzo AI / Zen LM
Capabilities:
- Image analysis, video comprehension, spatial reasoning
- OCR across 32 languages
- Multimodal reasoning for STEM/math/code
- Visual understanding and comprehensive image analysis
Format: SafeTensors
Downloads: Not tracked
Community: Multiple implementations
Inference: No provider deployment available
Status: Verified active on HuggingFace

4. zen-vl-8b-agent ✅ RELEASED

HuggingFace: https://huggingface.co/zenlm/zen-vl-8b-agent
Base: Qwen3-VL-8B-Instruct
Parameters: 8B (advertised as 9B params)
Architecture: Qwen3-VL based vision-language model
Context: 256K tokens (expandable to 1M)
Task Category: Image-Text-to-Text
License: Apache 2.0
Creator: Hanzo AI / Zen LM
Capabilities:
- Visual understanding and image analysis
- OCR across 32 languages
- Multimodal reasoning (STEM/math/code)
- Function calling for tool use
- Visual agent capabilities for GUI interaction
Format: SafeTensors
Downloads: Not tracked
Community: Active adoption
Inference: No provider deployment available
Status: Verified active on HuggingFace

5. zen-vl-30b-instruct ✅ RELEASED

HuggingFace: https://huggingface.co/zenlm/zen-vl-30b-instruct
Base: Qwen3-VL-30B
Parameters: 30B (31B MoE)
Architecture: Qwen3-VL based vision-language model
Context: 256K tokens (expandable to 1M)
Task Category: Image-Text-to-Text
License: Apache 2.0
Creator: Hanzo AI / Zen LM
Capabilities:
- Visual understanding: Image analysis, video comprehension, spatial reasoning
- OCR across 32 languages
- Multimodal reasoning for STEM/code tasks
- Comprehensive vision-language understanding
Format: SafeTensors
Downloads: Not tracked
Community: 2 Spaces using model
Inference: No provider deployment available
Paper: Coming soon
Status: Verified active on HuggingFace

6. zen-vl-30b-agent ✅ RELEASED

HuggingFace: https://huggingface.co/zenlm/zen-vl-30b-agent
Base: Qwen3-VL-30B-Instruct
Parameters: 30B (31B MoE)
Architecture: Qwen3-VL based with function calling
Context: 256K tokens (expandable to 1M)
License: Apache 2.0
Creator: Hanzo AI / Zen LM
Capabilities:
- Image analysis, video comprehension, spatial reasoning
- OCR across 32 languages
- Multimodal reasoning and tool-calling
- Visual context for agent tasks
- Fine-tuned with Zen identity and function calling
Format: SafeTensors
Downloads: Not tracked
Community: Active implementations
Inference: No provider deployment available
Status: Verified active on HuggingFace

Repository Structure

zen/
├── .github/
│   └── workflows/
│       ├── deploy.yml          # GitHub Pages deployment
│       ├── hf_model_eval.yml   # HuggingFace model evaluation
│       └── validate_models.yml # Model validation
├── docs/                       # Website (deployed to zenlm.org)
│   ├── index.html
│   ├── CNAME
│   └── assets/
│       ├── css/style.css
│       └── js/main.js
├── models/                     # Local model storage
│   ├── zen-nano/
│   ├── zen-eco/
│   ├── zen-omni/
│   ├── zen-coder/
│   └── zen-next/
├── scripts/                    # Utility scripts
├── training/                   # Training configurations and code
├── zen/                        # Qwen3 submodule
├── llama.cpp/                  # GGUF tools submodule
├── LLM.md                      # This file
└── README.md

Architecture

Qwen3 Foundation

All Zen AI models are built on the Qwen3 architecture:

Transformer-based: Modern decoder-only architecture
GQA (Grouped Query Attention): Efficient attention mechanism
32K Context: Extended context window support
Multi-lingual: Strong performance across languages
Open Source: Apache 2.0 licensed

Model Formats

SafeTensors (Primary)
- Full precision (bfloat16)
- Native PyTorch/Transformers support
- Best for training and fine-tuning
GGUF (Quantized)
- Multiple quantization levels (Q4_K_M, Q5_K_M, Q8_0, F16)
- Optimized for llama.cpp inference
- Excellent for CPU and edge deployment
MLX (Apple Silicon)
- Optimized for M1/M2/M3 chips
- Native Metal acceleration
- Best performance on Apple hardware

Key Technologies

Build & Inference

llama.cpp: GGUF conversion and CPU inference
MLX: Apple Silicon optimization
transformers: HuggingFace ecosystem
PyTorch: Training and fine-tuning

Development

Python: Training pipelines, data processing
Makefile: Build automation
GitHub Actions: CI/CD and deployment

Infrastructure

HuggingFace Hub: Model hosting and distribution
GitHub Pages: Documentation and website (zenlm.org)
Git Submodules: Dependency management (Qwen3, llama.cpp)

Development Workflow

Training Pipeline

Base model selection (Qwen3-{size})
Dataset preparation and curation
Fine-tuning with custom identity
Evaluation and benchmarking
Format conversion (SafeTensors → GGUF, MLX)
Upload to HuggingFace

Deployment Pipeline

Push to main branch
GitHub Actions triggers
Model validation runs
Website deploys to GitHub Pages
DNS points zenlm.org to GitHub Pages

Local Development

# Clone with submodules
git clone --recursive git@github.com:zenlm/zen.git

# Update submodules
git submodule update --init --recursive

# Build llama.cpp
cd llama.cpp
make

# Run local inference (example)
cd ..
./llama.cpp/llama-cli -m models/zen-nano/zen-nano-0.6b-q4_k_m.gguf -p "Tell me about Zen AI"

Essential Commands

Submodule Management

# Add new submodule
git submodule add <url> <path>

# Update all submodules
git submodule update --remote --merge

# Check submodule status
git submodule status

Model Conversion

# Convert to GGUF
python llama.cpp/convert_hf_to_gguf.py models/zen-nano --outfile zen-nano.gguf

# Quantize GGUF
./llama.cpp/llama-quantize zen-nano.gguf zen-nano-q4_k_m.gguf Q4_K_M

Website Development

# Serve locally (requires Python)
cd docs && python -m http.server 8000

# Or use Node.js
npx serve docs

Project Links

Website: https://zenlm.org
GitHub: https://github.com/zenlm/zen
HuggingFace: https://huggingface.co/zenlm
Qwen3: https://github.com/QwenLM/Qwen3
llama.cpp: https://github.com/ggerganov/llama.cpp

HuggingFace Model Verification (2025-11-15)

zen-nano (https://huggingface.co/zenlm/zen-nano)

Status: ✅ VERIFIED ACTIVE

Model Card & Documentation:

Complete model card with usage examples
Comprehensive documentation covering Transformers, llama.cpp, and LM Studio integration
Identity statement: "I'm Zen Nano, a 0.6B parameter model...optimized for edge computing"
Training methodology documented with zoo-gym framework
Citation guidelines for academic use
References to broader Zen model family

Files Present:

SafeTensors format (primary, ~596MB in bfloat16)
GGUF quantizations: F16 (1.19GB), Q8_0 (604MB), Q5_K_M (418MB), Q4_K_M (373MB)
Config.json and tokenizer files present
Chat template included for conversational use

Metadata:

Architecture: Qwen3ForCausalLM
Parameters: 600M (0.6B)
License: Apache 2.0
Framework: Transformers
Pipeline Tag: Text Generation
Context Length: 40,960 tokens (verified from API)
Language: English

Community Engagement:

Downloads: 70 last month
Community discussions: 1 active thread
Not deployed by inference providers but supports external deployment inquiries

zen-nano-0.6b (https://huggingface.co/zenlm/zen-nano-0.6b)