Frontier language models from 600M to 480B parameters. Open-weight models optimized for edge devices through cloud-scale deployments, built on Qwen3+ architecture with efficient inference via Rust, MLX, and GGUF.
Zen LM develops state-of-the-art language models spanning seven sizes from 600M (embedded) to 480B (frontier research). All models use the Qwen3+ architecture with RoPE embeddings, SwiGLU activation, grouped-query attention, and Flash Attention 2. Available through the Hanzo LLM Gateway, Hugging Face, vLLM, and local inference via MLX/GGUF.
# Using Hanzo LLM Gateway
from hanzo import Client
client = Client()
response = client.chat.completions.create(
model="zenlm/zen-7b",
messages=[{"role": "user", "content": "Hello!"}]
)
# Using Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("zenlm/zen-7b")
| Model |
Parameters |
Context |
Use Case |
| Zen-480B |
480B (MoE) |
128K |
Frontier research |
| Zen-70B |
70B |
128K |
Production |
| Zen-32B |
32B (MoE) |
64K |
Balanced |
| Zen-7B |
7B |
32K |
Fast inference |
| Zen-3B |
3B |
16K |
Edge devices |
| Zen-1.5B |
1.5B |
8K |
Mobile |
| Zen-600M |
600M |
4K |
Embedded |
| Project |
Description |
| zen |
Zen AI model family — efficient models for edge and cloud |
| engine |
High-performance inference engine — Rust/MLX/GGUF |
| gym |
Unified fine-tuning for 100+ LLMs and VLMs |
| zen-omni |
Zen-Omni 30B — hypermodal AI with MLX/GGUF |
| docs |
Documentation and model cards |
| Model |
MMLU |
HumanEval |
GSM8K |
MT-Bench |
| Zen-70B |
82.3 |
71.2 |
85.4 |
8.9 |
| Zen-32B |
78.1 |
65.8 |
79.2 |
8.4 |
| Zen-7B |
68.5 |
52.4 |
62.1 |
7.8 |
| Zen-3B |
58.2 |
38.6 |
48.3 |
7.1 |
| Organization |
Focus |
| Hanzo AI |
AI infrastructure — LLM gateway, MCP, agents |
| Lux Network |
Post-quantum blockchain, FHE, cross-chain |
| Zoo Labs |
Open AI research network (501c3) |
Apache 2.0 for code, model-specific licenses for weights.
Co-developed by Hanzo AI and Zoo Labs