Zen 6 Nano (2B) (zenlm/zen6-nano-2b)

Zen 6 Nano (2B) is an open-weight Apache-2.0 1.58-bit ternary multimodal & agentic language model (2B ternary tier) developed by Hanzo AI and Zen LM in collaboration with Zoo Labs Foundation.

Post-trained directly in the Sylvester-Walsh-Hadamard ternary weight space via Straight-Through Estimator (STE) Quantization-Aware Training (QAT) with Hybrid Muon + AdamW, zen6-nano combines:

• Authentic Weight-Space Identity: Native zen6-nano provenance with zero system-prompt wrapper overhead, while strictly respecting custom developer personas (e.g., Godot game NPCs, JSON extractors).
• Agentic Tool Calling & Hanzo MCP: Multi-step <tool_call> execution, <tool_response> synthesis, error recovery, and calibrated tool abstention across fs, exec, code, git, test, hanzo, and game-engine director tools.
• Runtimes: PQ2_0 / PTQ1_0 / Q2_0 run today on the PrismML llama.cpp fork (branch prism: CUDA, Metal, CPU); Q2_0 also runs on upstream llama.cpp b11556+. MLX 2-bit packs run with mlx-lm on Apple Silicon. Native hanzo-engine ternary kernels (CUDA / ROCm / Metal / SIMD CPU) are in progress.
• Zero-Token-Burn Cloud CI/CD: Load zen6-nano-2b-PTQ1_0.gguf (~429 MB) on CPU runners for end-to-end LLM and tool-calling integration tests with no API keys or per-token cost.

Weight Artifacts

File	Format	Description
zen6-nano-2b-PQ2_0.gguf	PQ2_0	Fast GPU/CPU ternary pack
zen6-nano-2b-PTQ1_0.gguf	PTQ1_0	Smallest dense-trit pack (CPU / CI)
zen6-nano-2b-Q2_0.gguf	Q2_0	Upstream llama.cpp compatible
zen6-nano-2b-mmproj-Q8_0.gguf	Q8_0	Vision projector
zen6-nano-2b-mmproj-BF16.gguf	BF16	Vision projector (reference)

Quickstart — Local Stack & Game Backend (hanzo up)

# Pull & serve zenlm/zen6-nano-2b locally on http://127.0.0.1:36900/v1
hanzo pull zenlm/zen6-nano-2b
hanzo engine serve zenlm/zen6-nano-2b

# Or bring up the full local Hanzo stack + open-weight AI
hanzo up

OpenAI-Compatible /v1/chat/completions + MCP Tools

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:36900/v1", api_key="hanzo-local")
resp = client.chat.completions.create(
    model="zen6-nano",
    messages=[{"role": "user", "content": "Check Cargo.toml and run the workspace unit tests."}],
    tools=[{
        "type": "function",
        "function": {
            "name": "fs",
            "description": "Read or list workspace files",
            "parameters": {
                "type": "object",
                "properties": {"action": {"type": "string"}, "path": {"type": "string"}},
                "required": ["action", "path"],
            },
        },
    }],
)
print(resp.choices[0].message)

Zero-Token-Burn GitHub Actions CPU CI

- name: Serve open-weight Zen 6 Nano on CPU
  run: |
    hf download hanzoai/zen6-nano zen6-nano-2b-PTQ1_0.gguf --local-dir ./models
    llama-server -m ./models/zen6-nano-2b-PTQ1_0.gguf -ngl 0 --port 36900 --alias zen6-nano &

Architecture & Hadamard Ternary Specification

• Hybrid Backbone: Interleaved Gated-DeltaNet (GDN) linear-attention blocks (3x) and Grouped-Query Full Attention blocks (1x) with SwiGLU MLPs and multimodal vision projector (mmproj).
• Hadamard Outlier Rotation: Linear weights and token embeddings are stored in an orthonormal block Sylvester-Walsh-Hadamard basis ($H_B[r,c] = (-1)^{\text{popcount}(r & c)}/\sqrt{B}$ with explicit per-channel $\pm 1$ sign vectors embedded in GGUF KV metadata prism.hadamard.*), eliminating activation outliers so group-128 ternary {-d, 0, +d} weights retain full dense precision.
• Production Formats:
  • PQ2_0 (GGML type 142, 34 B / 128 weights = 2.125 bpw): Fastest SIMD / CUDA dp4a / ROCm / Metal execution (zen6-nano-9b-PQ2_0.gguf).
  • PTQ1_0 (GGML type 143, 28 B / 128 weights = 1.750 bpw): Smallest disk & RAM footprint (5 trits/byte base-3 packing).
  • Q2_0 (GGML type 42, 18 B / 64 weights = 2.250 bpw): Group-64 compatibility band.

License & Attribution

Released under the Apache License 2.0 (LICENSE). Preserves full upstream attribution to Qwen (Alibaba Cloud), Bonsai-2 (Prism ML, Inc.), and DFlash / ProCreations (NOTICE).